Ecology

Material characterization resultsTo investigate the structural composition of NMC-2, XRD analysis plots were performed. Figure 1a shows the XRD pattern of the NMC-2 composite before adsorption. The XRD pattern shows the corresponding strong and narrow peaks, from which it can be seen that the peaks of broad diffraction NMC-2 can correspond to the standard cards of Fe, C, Fe7C3, Fe2C, and FeC, indicating that the synthesized adsorbent is an iron-carbon composite. It can be indicated that mesoporous nitrogen-doped composites were formed during the carbonization process. During the experiments, it was found that the materials are magnetic, probably because of the presence of Fe, FeC, Fe7C3, Fe2C. Due to the magnetic properties of this type of material, rapid separation and recovery can be obtained under the conditions of an applied magnetic field, which allows easy separation of the adsorbent and metal ions from the wastewater15.Figure 1XRD and nitrogen adsorption and desorption tests on materials: (a) XRD pattern of NMC-2 adsorbent before adsorption, (b) pore size distribution of NMC-2, (c) nitrogen adsorption–desorption curve of NMC-2 adsorbent.Full size imageFrom the adsorption–desorption curves of adsorbent N2 in Fig. 1b, it can be seen that the NMC-2 isotherm belongs to the class IV curve, and the appearance of H3-type hysteresis loops is observed at the medium pressure end, and H3 is commonly found in aggregates with laminar structure, producing slit mesoporous or macroporous materials, which indicates that N2 condenses and accumulates in the pore channels, and these phenomena prove that NMC-2 is a porous material16. Figure 1c shows the pore size distribution of the adsorbent NMC-2 obtained according to the BJH calculation method, from which it can be seen that the pore size distribution is not uniform in the range, and most of them are concentrated below 20 nm, while according to Table 1, the specific surface area of the original sample of Fenton sludge and fly ash is 124.08 m2/g and 3.79 m2/g, respectively, and the specific surface area of NMC-2 is 228.65 m2/g. The Fenton The pore volume of the original samples of Fenton sludge and fly ash were 0.18 cm3/g and 0.006 cm3/g respectively, while the pore volume of NMC-2 was 0.24 cm3/g. The pore diameters of the original sample of Fenton sludge and fly ash were 5.72 nm and 6.70 nm respectively, while the pore diameter of NMC-2 was 4.22 nm. The above data indicated that the synthetic materials have increased the specific surface area and pore volume compared with the original samples, indicating that the doping of nitrogen can increase the specific surface area of the material. Since the pore size of mesoporous materials is 2–50 nm, NMC-2 is a porous material with main mesopores. Thanks to the large specific surface area provided by the mesopores, the material has a large number of active sites, and in addition, the mesopores can store more Cr(VI)16, which contributes to efficient removal.Table 1 Total pore-specific surface area, pore volume, and pore size of BJH adsorption and accumulation of Fenton sludge, fly ash and NMC-2.Full size tableThe morphological analysis of the material surface using SEM can see the surface structure and the pore structure of NMC-2. And Fig. 2a–d shows the swept electron microscope image of NMC-2. Figure 2a shows that the surface of the material is not smooth, and there are more lint-like fiber structures. The fibers in Fig. 2b are loosely and irregularly arranged, which may be due to the irregular morphology caused by the small particles of the NMC-2 sample. As shown in Fig. 2c and Fig. 2a there are more pores generated on the surface of NMC-2, which may be due to the addition of K2CO3 to urea and, Fenton sludge solution to generate CO217.Figure 2SEM, TEM and EDS testing of materials: (a–d) SEM image of NMC-2 adsorbent, (e) TEM image of NMC-2; (g–i) TEM-EDS spectrum of NMC-2, (j) TEM-EDS spectra of NMC-2 obtained from.Full size imageThese pores can provide many active sites, which is consistent with the results derived in Fig. 1, where NMC-2 is a mesoporous-dominated porous material, and also demonstrates that the addition of urea can provide a nitrogen source for the material, providing abundant active sites. Figure 2j depicts the TEM of NMC-2. the TEM images show that the synthesized NMC-2 has a folded structure with a surface covered by a carbon film, and the HRTEM (Fig. 2e) also confirms this result with a lattice spacing of 0.13, 0.15, 0.20, 0.23, 0.24, and 0.25 nm, corresponding to the (4 5 2) and (1 0 2) of C, the (2 0 1) of FeC) surface, the (2 1 0) surface of Fe7C3, the (5 3 1) surface of Fe2C, and the (2 0 1) surface of FeC, which also confirms the synthesis of the above substances. The corresponding EDS spectra of the dark field diagram NMC-2 were obtained from Fig. 2j, and the EDS spectra proved the presence of various elements: carbon (C) (Fig. 2f) from fly ash, iron (Fe) (Fig. 2g) from Fenton sludge, nitrogen (N) (Fig. 2h) from urea, and the presence of (O) (Fig. 2i), further confirming the successful preparation of NMC-2.The type of functional groups and chemical bonding on the surface of the material can be analyzed by IR spectrogram analysis. Figure 3b shows the FTIR image of NMC-2 adsorbent 3440 cm−1 wide and strong absorption peak is due to the stretching vibration of –OH, there is a large amount of –OH present on the surface of the material; the peak appearing at 1640 cm−1 is –COOH. Characterization reveals that the –OH absorption peak is wider18,19. In addition, the absorptions at 1390 cm−1 and 1000 cm−1 were attributed to the bending of –OH vibrations of alcohols and phenol and the stretching vibration of C–O20. The above results indicate that the surface of NMC-2 contains a large number of oxygen-containing functional groups, and these functional groups can provide many active sites for the removal of Cr(VI). It was also found that the weak peaks corresponding to 573 cm−1 and 550 cm−1 were attributed to Fe–O groups21. The stretching of Fe–O may be due to the oxidation of loaded Fe0 and Fe2+ to Fe3+22. Figure 3a shows the Fenton sludge and fly ash FTIR images. It can be seen from the figure that the surfaces of Fenton sludge and fly ash contain a large number of oxygen-containing functional groups, the surface functional groups of the two raw materials are more abundant, and the functional groups of NMC-2 around 3441 cm−1, 1632 cm−1, and 1400 cm−1 are not significantly different from those of the raw materials, and the C–H stretching vibration peaks of NMC-2 around 873 cm−1 and 698 cm−1 is not obvious, which may be because the material the C–H bond on the surface of the raw material was oxidized to C–O in the process of synthesis.Figure 3FTIR testing of materials: (a) FTIR image of Fenton sludge, fly ash, (b) Ftir image of NMC-2 adsorbent.Full size imageCr(VI) adsorption experimentSelection of adsorbentTo select the best adsorbent, Cr(VI) adsorption tests were performed on four adsorbents. Figure 4a shows the effect of Fenton sludge and the urea addition on the adsorption efficiency. The Cr(VI) removal rates of the four adsorbents were ranked from low to high: MC-1  More

A map of the Amazon River and its tributaries, as published in Alfred Russel Wallace’s 1853 book.Credit: Mary Evans/Natural History Museum

Dzoodzo Baniwa, a member of an Indigenous community in Brazil’s Amazonas state, has been collecting data on the region’s biodiversity for around 15 years. He lives in a remote village called Canadá on the Ayari River, a tributary of the Içana, which in turn feeds the Rio Negro, one of the main branches of the Amazon. The nearest city, São Gabriel da Cachoeira, is a three-day trip by motor boat.Dzoodzo (who goes by his Indigenous name but is also known as Juvêncio Cardoso) takes inspiration for his work from many cross-cultural sources. A perhaps unexpected one is a 170-year-old book by the British naturalist Alfred Russel Wallace, who visited the Amazon and Negro rivers on his expeditions in 1848–52. A Narrative of Travels on the Amazon and Rio Negro gives detailed accounts of the wildlife and people Wallace encountered near Dzoodzo’s home, including the Guianan cock-of-the-rock (Rupicola rupicola), a bright orange bird that Wallace describes as “magnificent … sitting amidst the gloom, shining out like a mass of brilliant flame”1.Dzoodzo’s passion for local biodiversity is reflected in his work at Baniwa Eeno Hiepole School, an internationally praised education centre for Indigenous people. He dreams of one day turning it into a research institute and university that might increase scientific understanding of the region’s species, including R. rupicola.
Alfred Russel Wallace’s first expedition ended in flames
Wallace, who was born 200 years ago, on 8 January 1823, is best known for spurring Charles Darwin into finally publishing On the Origin of Species, after Wallace sent Darwin his own independent discovery of evolution by natural selection in 1858. Most of Wallace’s subsequent work drew on observations from his 1854–62 expeditions in southeast Asia; his earlier work in Amazonia is much less well known.Yet there are lessons from Wallace’s time in Brazil that are especially relevant for conservationists and other scientists today — notably, what can come from paying attention to what local people say about their own territory.Barriers and boundariesWallace made two key contributions that still shape thinking about Amazonia, the world’s most biodiverse region, which covers parts of Bolivia, Brazil, Colombia, Ecuador, Peru, Venezuela, Guyana, Suriname and French Guiana.On 14 December 1852, Wallace read out his manuscript ‘On the monkeys of the Amazon’ at a meeting of the Zoological Society of London. In this study, which was later published2, Wallace relays observations that form the basis of the most debated hypothesis for how Amazonian organisms diversified: the riverine barrier hypothesis.His paper refers to the large Amazonian rivers as spatial boundaries to the ranges of several primate species. “I soon found that the Amazon, the Rio Negro and the Madeira formed the limits beyond which certain species never passed,” he writes. Since 1852, Wallace’s observations that large rivers could act as geographical barriers that shape the distribution of species have been corroborated, criticized and debated by many. The phenomenon he described clearly holds for some groups, such as monkeys and birds3,4, but not for other groups, such as plants and insects5.Subsequent researchers have explored whether the distribution patterns of species, such as those observed by Wallace, indicate that the evolution of the Amazonian drainage system has itself driven the diversification of species6. Work in the past few years by geologists and biologists show that this drainage system, which includes some of the largest rivers in the world, is dynamic7, and that its rearrangements lead to changes in the distribution ranges of species8. Current species ranges thus hold information about how the Amazonian landscape has changed over time.

The Guianan cock-of-the-rock (Rupicola rupicola), which Wallace likened to a “brilliant flame”.Credit: Hein Nouwens/Getty

The second crucial observation made by Wallace, also in his 1852 paper, was that the composition of species varies in different regions. He describes how “several Guiana species come up to the Rio Negro and Amazon, but do not pass them; Brazilian species on the contrary reach but do not pass the Amazon to the north. Several Ecuador species from the east of the Andes reach down into the tongue of land between the Rio Negro and Upper Amazon, but pass neither of those rivers, and others from Peru are bounded on the north by the Upper Amazon, and on the east by the Madeira.” From these observations, he concluded that “there are four districts, the Guiana, the Ecuador, the Peru and the Brazil districts, whose boundaries on one side are determined by the rivers I have mentioned.”
Evolution’s red-hot radical
Even though Amazonia is presented as a single, large, green ellipse in most world maps, it is actually a heterogeneous place, with each region and habitat type holding a distinct set of species9,10. The four districts proposed by Wallace are bounded by the region’s largest rivers: the Amazon, Negro and Madeira. But further studies of species ranges since then have revealed more districts, now called areas of endemism, some of which are also bounded by these and other large Amazonian rivers, such as the Tapajós, Xingu and Tocantins9,11.This recognition of spatial heterogeneity in Amazonian species distributions — first accomplished by Wallace — is essential for today’s research, conservation and planning10. Each area of endemism includes species that occur only in that area. And different areas of endemism are affected differently by anthropogenic impacts, such as deforestation, fires and development10. More than half of Amazonia is now within federal or state reserves or Indigenous lands — territories that are recognized by current governments as belonging to Indigenous people. But nearly half of the region’s areas of endemism are located in the south of the region, close to the agricultural frontier, and the species they contain are severely threatened by habitat loss10 (see also www.raisg.org/en).Local knowledgeAlthough Wallace’s writings indicate that in many ways he admired most of the Indigenous people he met, especially those from the upper Rio Negro basin, he still viewed Indigenous people through the European colonial lens of his time. In A Narrative of Travels on the Amazon and Rio Negro1, Wallace describes the Indigenous communities he encountered as “in an equally low state of civilization” — albeit seemingly “capable of being formed, by education and good government, into a peaceable and civilized community”.Yet he did better than many of his contemporaries when it came to respecting local knowledge. In his 1852 paper, for example, Wallace notes that his fellow European naturalists often give vague information about the locality of their collected specimens, and fail to specify such localities in relation to river margins. By contrast, he writes, the “native hunters are perfectly acquainted” with the impact of rivers on the distribution of species, “and always cross over the river when they want to procure particular animals, which are found even on the river’s bank on one side, but never by any chance on the other.” Likewise, in his 1853 book1, Wallace frequently corroborates his findings with information he has obtained from Indigenous people — for example, about the habitat preferences of umbrellabirds (Cephalopterus ornatus) or of “cow-fish” (manatees; Trichechus inunguis).Considering the vastness and complexity of Amazonia, it is hard to see how Wallace could have gained the insights he did after working in the region for only four years, had he not paid close attention to local knowledge.
The other beetle-hunter
Amazonian Indigenous peoples have had to endure invasion of their lands, enslavement, violence from invaders and the imposition of other languages and cultures. Despite this, numerous Indigenous researchers wish to expand their knowledge about Amazonia by combining Indigenous and European world views. Meanwhile, a better understanding of how the Amazonian socio-ecological system is organized, and how it is being affected by climate change and local and regional impacts12, hinges on the ability of researchers worldwide to learn from and to be led by Indigenous scientists.The 98 Indigenous lands in the Rio Negro basin cover more than 33 million hectares (see go.nature.com/3wkkftu). If the hopes of Dzoodzo and others to build a research institute and university for the region are met, school students will no longer have to leave their homeland to pursue higher education. The community would have a way to document its own knowledge and that of its ancestors in a more systematic way. And the legitimization of Indigenous people’s research efforts in the legal and academic frameworks recognized by non-Indigenous scientists — such as through the awarding of degrees — would make it easier for Indigenous researchers to partner with other organizations, both nationally and internationally.Indigenous people in the Rio Negro basin today are no longer objects of observation — they have taken charge of their own research using tools from different cultures. Indeed, Dzoodzo is turning to Wallace’s writings, in part, to learn more about how his own ancestors lived.Perhaps the thread between Wallace and Dzoodzo, spanning so many years and such disparate cultures, could seed new kinds of partnership in which learning is reciprocal and for the benefit of all. More

EU Commission. Achieve Good Environmental Status. EU Commission Web site https://ec.europa.eu/environment/marine/good-environmental-status/index_en.htm (2008).Olenin, S. et al. Marine strategy framework directive. Task Group 2 (2010).Long, R. The marine strategy framework directive: a new european approach to the regulation of the marine environment, marine natural resources and marine ecological services. Journal of Energy & Natural Resources Law 29, 1–44 (2011).Article 
ADS 

Google Scholar 
Borja, A. et al. Good environmental status of marine ecosystems: what is it and how do we know when we have attained it? Marine Pollution Bulletin 76, 16–27 (2013).Article 

Google Scholar 
Froese, R., Demirel, N., Coro, G. & Kleisner, K. M. & Winker, H. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18, 506–526 (2017).Article 

Google Scholar 
Coro, G. Open science and artificial intelligence supporting blue growth. Environmental Engineering & Management Journal (EEMJ) 19 (2020).JRC. EU data collection Web site https://datacollection.jrc.ec.europa.eu/ – Accessed June 2022 (2021).EcoScope Consortium. The EcoScope EU project Web site https://ecoscopium.eu/ – Accessed June 2022 (2021).Pikitch, E. K. et al. Ecosystem-based fishery management. Science 305, 346–347 (2004).Article 

Google Scholar 
McLeod, K. L. & Leslie, H. M. Why ecosystem-based management. Ecosystem-based management for the oceans 3–12 (2009).Coro, G., Magliozzi, C., Ellenbroek, A. & Pagano, P. Improving data quality to build a robust distribution model for architeuthis dux. Ecological modelling 305, 29–39 (2015).Article 

Google Scholar 
Coro, G., Ellenbroek, A. & Pagano, P. An open science approach to infer fishing activity pressure on stocks and biodiversity from vessel tracking data. Ecological Informatics 64, 101384 (2021).Article 

Google Scholar 
Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics 40, 677–697 (2009).Article 

Google Scholar 
Coro, G., Bove, P. & Ellenbroek, A. Habitat distribution change of commercial species in the adriatic sea during the covid-19 pandemic. Ecological Informatics 101675 (2022).Stanton, J. C., Pearson, R. G., Horning, N., Ersts, P. & Reşit Akçakaya, H. Combining static and dynamic variables in species distribution models under climate change. Methods in Ecology and Evolution 3, 349–357 (2012).Article 

Google Scholar 
Coro, G., Magliozzi, C., Ellenbroek, A., Kaschner, K. & Pagano, P. Automatic classification of climate change effects on marine species distributions in 2050 using the aquamaps model. Environmental and ecological statistics 23, 155–180 (2016).Article 
MathSciNet 

Google Scholar 
Coro, G., Pagano, P. & Ellenbroek, A. Detecting patterns of climate change in long-term forecasts of marine environmental parameters. International Journal of Digital Earth 13, 567–585 (2020).Article 
ADS 

Google Scholar 
Wayte, S. E. Management implications of including a climate-induced recruitment shift in the stock assessment for jackass morwong (nemadactylus macropterus) in south-eastern australia. Fisheries Research 142, 47–55 (2013).Article 

Google Scholar 
Tanaka, K. R. Integrating environmental information into stock assessment models for fisheries management. Predicting Future Oceans 193–206 (2019).Szuwalski, C. S. & Hollowed, A. B. Climate change and non-stationary population processes in fisheries management. ICES Journal of Marine Science 73, 1297–1305 (2016).Article 

Google Scholar 
Bevilacqua, A. H. V., Carvalho, A. R., Angelini, R. & Christensen, V. More than anecdotes: fishers’ ecological knowledge can fill gaps for ecosystem modeling. PLoS One 11, e0155655 (2016).Article 

Google Scholar 
Heymans, J. J. et al. Best practice in ecopath with ecosim food-web models for ecosystem-based management. Ecological Modelling 331, 173–184 (2016).Article 

Google Scholar 
Piroddi, C. et al. Historical changes of the mediterranean sea ecosystem: modelling the role and impact of primary productivity and fisheries changes over time. Scientific reports 7, 1–18 (2017).Article 
ADS 

Google Scholar 
Campana, E. F., Ciappi, E. & Coro, G. The role of technology and digital innovation in sustainability and decarbonization of the blue economy. Bulletin of Geophysics and Oceanography 123 (2021).Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic change 109, 5–31 (2011).Article 
ADS 

Google Scholar 
Intergovernmental Panel on Climate Change https://www.academia.edu/download/60673993/climate_change_emission_Special_scenarios20190922-59363-1j1i98f.pdf – Accessed October 2022. IPCC Special Report (2000).Scarcella, G. et al. The potential effects of covid-19 lockdown and the following restrictions on the status of eight target stocks in the adriatic sea. Frontiers in Marine Science 1963 (2022).Wikipedia. ESRI-GRID formats description. Wikipedia https://en.wikipedia.org/wiki/Esri_grid (2022).QGIS. Qgis software version 3.20.0. QGIS Web site https://www.qgis.org/en/site/ (2022).ESRI. Arcgis software version 10.7. ArcGIS Web site https://www.esri.com/it-it/arcgis/products/arcgis-desktop/overview (2022).American Museum of Natural History. Maxent software for modelling species distributions. AMNH Web site https://biodiversityinformatics.amnh.org/open_source/maxent/ (2022).Christensen, V. et al. Ecopath with ecosim: a user’s guide. Fisheries Centre, University of British Columbia, Vancouver 154, 31 (2005).
Google Scholar 
Coll, M., Bundy, A. & Shannon, L. J. Ecosystem modelling using the ecopath with ecosim approach. In Computers in fisheries research, 225–291 (Springer, 2009).Colléter, M. et al. Global overview of the applications of the ecopath with ecosim modeling approach using the ecobase models repository. Ecological Modelling 302, 42–53 (2015).Article 

Google Scholar 
VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L. & Storlie, C. Sdmtools: Species distribution modelling tools: Tools for processing data associated with species distribution modelling exercises. R package version 1, 1 (2014).
Google Scholar 
US National Institutes of Health. ImageJ software for image analysis with Java and the Terrain Cartography plugin for reading ASC files. ImageJ Web site https://imagej.nih.gov/ij/index.html (2018).GDAL. Translator library for raster and vector geospatial data, version 3.5.0. GDAL Web site https://gdal.org/ (2022).Claus, S. et al. Marine regions: towards a global standard for georeferenced marine names and boundaries. Marine Geodesy 37, 99–125 (2014).Article 

Google Scholar 
VLIZ. World marine regions definitions and geospatial data. Marine Regions Web site www.marineregions.org (2022).Coro, G. The ASCFileManagement GitHub repository. GitHub https://github.com/cybprojects65/ASCFileManagement (2022).Ready, J. et al. Predicting the distributions of marine organisms at the global scale. Ecological Modelling 221, 467–478 (2010).Article 

Google Scholar 
Selig, E. R. et al. Global priorities for marine biodiversity conservation. PloS one 9, e82898 (2014).Article 
ADS 

Google Scholar 
O’hara, C. C., Afflerbach, J. C., Scarborough, C., Kaschner, K. & Halpern, B. S. Aligning marine species range data to better serve science and conservation. PLoS One 12, e0175739 (2017).Article 

Google Scholar 
Scarponi, P., Coro, G. & Pagano, P. A collection of aquamaps native layers in netcdf format. Data in brief 17, 292–296 (2018).Article 

Google Scholar 
CMEMS. Copernicus Marine Service ocean products data. Copernicus Marine Service Web site https://marine.copernicus.eu/ (2022).E.U. Copernicus Marine Service Information. Global Ocean 1/12° Physics Analysis and Forecast updated Daily. Copernicus Marine Service Web site https://doi.org/10.48670/moi-00016 (2021).Article 

Google Scholar 
E.U. Copernicus Marine Service Information. Global Ocean Biogeochemistry Analysis and Forecast. Copernicus Marine Service Web site https://doi.org/10.48670/moi-00015 (2021).Article 

Google Scholar 
Hijmans, R. J. et al. Terra: Spatial data analysis. R Spatial Data Science Web site https://rspatial.org/terra/ (2022).MacLeod, C. D. Habitat representativeness score (hrs): a novel concept for objectively assessing the suitability of survey coverage for modelling the distribution of marine species. Journal of the Marine Biological Association of the United Kingdom 90, 1269–1277 (2010).Article 

Google Scholar 
Abdi, H. & Williams, L. J. Principal component analysis. Wiley interdisciplinary reviews: computational statistics 2, 433–459 (2010).Article 

Google Scholar 
Coro, G., Pagano, P. & Ellenbroek, A. Combining simulated expert knowledge with neural networks to produce ecological niche models for latimeria chalumnae. Ecological modelling 268, 55–63 (2013).Article 

Google Scholar 
Coro, G., Pagano, P. & Ellenbroek, A. Automatic procedures to assist in manual review of marine species distribution maps. In International Conference on Adaptive and Natural Computing Algorithms, 346–355 (Springer, 2013).Magliozzi, C., Coro, G., Grabowski, R. C., Packman, A. I. & Krause, S. A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning. Environmental Modelling & Software 111, 311–323 (2019).Article 

Google Scholar 
Coro, G. & Bove, P. Global-Scale Parameters for Ecological Models, FigShare, https://doi.org/10.6084/m9.figshare.c.6039275.v4 (2022).Coro, G. Means, standard deviations, geometric means, and log-normal standard deviation of the data produced for the present publication. D4Science distributed storage system https://data.d4science.net/foLS (2022).Mann, M. E., Bradley, R. S. & Hughes, M. K. Global-scale temperature patterns and climate forcing over the past six centuries. Nature 392, 779–787 (1998).Article 
ADS 

Google Scholar 
Biskaborn, B. K. et al. Permafrost is warming at a global scale. Nature communications 10, 1–11 (2019).Article 

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

Google Scholar 
Huang, Y., Zhang, W., Sun, W. & Zheng, X. Net primary production of chinese croplands from 1950 to 1999. Ecological Applications 17, 692–701 (2007).Article 

Google Scholar 
Sunlu, U., Aksu, M., Buyukisik, B. & Sunlu, F. S. Spatio-temporal variations of organic carbon and chlorophyll degradation products in the surficial sediments of izmir bay (aegean sea/turkey). Environmental monitoring and assessment 146, 423–432 (2008).Article 

Google Scholar 
Kubryakov, A., Mikaelyan, A., Stanichny, S. & Kubryakova, E. Seasonal stages of chlorophyll-a vertical distribution and its relation to the light conditions in the black sea from bio-argo measurements. Journal of Geophysical Research: Oceans 125, e2020JC016790 (2020).ADS 

Google Scholar 
Gamo, T. Global warming may have slowed down the deep conveyor belt of a marginal sea of the northwestern pacific: Japan sea. Geophysical Research Letters 26, 3137–3140 (1999).Article 
ADS 

Google Scholar 
Mahaffey, C., Palmer, M., Greenwood, N. & Sharples, J. Impacts of climate change on dissolved oxygen concentration relevant to the coastal and marine environment around the uk. MCCIP Science Review 2002, 31–53 (2020).
Google Scholar 
Zhang, W., Dunne, J. P., Wu, H., Zhou, F. & Huang, D. Using timescales of deficit and residence to evaluate near-bottom dissolved oxygen variation in coastal seas. Journal of Geophysical Research: Biogeosciences 127, e2021JG006408 (2022).ADS 

Google Scholar 
Helm, K. P., Bindoff, N. L. & Church, J. A. Changes in the global hydrological-cycle inferred from ocean salinity. Geophysical Research Letters 37 (2010).Ren, L., Speer, K. & Chassignet, E. P. The mixed layer salinity budget and sea ice in the southern ocean. Journal of Geophysical Research: Oceans 116 (2011).Mahmuduzzaman, M. et al. Causes of salinity intrusion in coastal belt of bangladesh. International Journal of Plant Research 4, 8–13 (2014).
Google Scholar 
Podymov, O., Zatsepin, A. & Ocherednik, V. Increase of temperature and salinity in the active layer of the north-eastern black sea from 2010 to 2020. Physical Oceanography 28, 257–265 (2021).Article 

Google Scholar 
Mizyuk, A. & Puzina, O. Sea ice modeling in the sea of azov for a study of long-term variability. In IOP Conference Series: Earth and Environmental Science, vol. 386, 012023 (IOP Publishing, 2019).Pärn, O., Friedland, R., Rjazin, J. & Stips, A. Regime shift in sea-ice characteristics and impact on the spring bloom in the baltic sea. Oceanologia 64, 312–326 (2022).Article 

Google Scholar 
Lundesgaard, Ø., Sundfjord, A. & Renner, A. H. Drivers of interannual sea ice concentration variability in the atlantic water inflow region north of svalbard. Journal of Geophysical Research: Oceans 126, e2020JC016522 (2021).ADS 

Google Scholar 
Schwegmann, S. & Holfort, J. Regional distributed trends of sea ice volume in the baltic sea for the 30-year period 1982 to 2019. Meteorologische Zeitschrift 33–43 (2021).Simon, S. Interpretation of the correlation coefficient. PMean Web site http://www.pmean.com/definitions/correlation.htm (2020).Yacobi, Y. et al. Chlorophyll distribution throughout the southeastern mediterranean in relation to the physical structure of the water mass. Journal of Marine Systems 6, 179–190, https://doi.org/10.1016/0924-7963(94)00028-A (1995).Article 
ADS 

Google Scholar 
Kucuksezgin, F., Balci, A., Kontas, A. & Altay, O. Distribution of nutrients and chlorophyll-a in the aegean sea. Oceanologica Acta 18, 343–352 (1995).
Google Scholar 
Villate, F., Aravena, G., Iriarte, A. & Uriarte, I. Axial variability in the relationship of chlorophyll a with climatic factors and the north atlantic oscillation in a basque coast estuary, bay of biscay (1997–2006). Journal of Plankton Research 30, 1041–1049 (2008).Article 

Google Scholar 
Iriarte, A. et al. Dissolved oxygen in contrasting estuaries of the bay of biscay: effects of temperature, river discharge and chlorophyll a. Marine Ecology Progress Series 418, 57–71 (2010).Article 
ADS 

Google Scholar 
Stanev, E. V. Black sea dynamics. Oceanography 18, 56–75 (2005).Article 

Google Scholar 
Tsimplis, M. N. & Rixen, M. Sea level in the mediterranean sea: The contribution of temperature and salinity changes. Geophysical research letters 29, 51–1 (2002).Article 

Google Scholar 
Schneider, A., Wallace, D. W. & Körtzinger, A. Alkalinity of the mediterranean sea. Geophysical Research Letters 34 (2007).Sara, G., Porporato, E. M., Mangano, M. C. & Mieszkowska, N. Multiple stressors facilitate the spread of a non-indigenous bivalve in the mediterranean sea. Journal of Biogeography 45, 1090–1103 (2018).Article 

Google Scholar 
Soto-Navarro, J. et al. Evolution of mediterranean sea water properties under climate change scenarios in the med-cordex ensemble. Climate Dynamics 54, 2135–2165 (2020).Article 
ADS 

Google Scholar 
Dietze, H. & Löptien, U. Retracing hypoxia in eckernförde bight (baltic sea). Biogeosciences 18, 4243–4264 (2021).Article 
ADS 

Google Scholar 
Ulses, C. et al. Oxygen budget of the north-western mediterranean deep-convection region. Biogeosciences 18, 937–960 (2021).Article 
ADS 

Google Scholar 
Jaskulak, M., Sotomski, M., Michalska, M., Marks, R. & Zorena, K. The effects of wastewater treatment plant failure on the gulf of gdansk (southern baltic sea). International Journal of Environmental Research and Public Health 19, 2048 (2022).Article 

Google Scholar 
Mihanović, H. et al. Observation, preconditioning and recurrence of exceptionally high salinities in the adriatic sea. Frontiers in Marine Science 8, 834 (2021).Article 

Google Scholar 
De Leo, F., Besio, G. & Mentaschi, L. Trends and variability of ocean waves under rcp8. 5 emission scenario in the mediterranean sea. Ocean Dynamics 71, 97–117 (2021).Article 
ADS 

Google Scholar 
Omar, A. M. et al. Trends of ocean acidification and pco2 in the northern north sea, 2003–2015. Journal of Geophysical Research: Biogeosciences 124, 3088–3103 (2019).Article 

Google Scholar 
Kröncke, I. et al. Comparison of biological and ecological long-term trends related to northern hemisphere climate in different marine ecosystems. Nature Conservation (2019).Bonnet, D. et al. Comparative seasonal dynamics of centropages typicus at seven coastal monitoring stations in the north sea, english channel and bay of biscay. Progress in oceanography 72, 233–248 (2007).Article 
ADS 

Google Scholar 
Borja, Á. et al. Implementation of the european marine strategy framework directive: A methodological approach for the assessment of environmental status, from the basque country (bay of biscay). Marine Pollution Bulletin 62, 889–904, https://doi.org/10.1016/j.marpolbul.2011.03.031 (2011).Article 

Google Scholar 
Coro, G. An open-source re-implementation of the habitat representativeness score. GitHub https://github.com/cybprojects65/HabitatRepresentativenessScore (2022).Coro, G. An OGC-WPS compliant interface to calculate Habitat Representativeness Score. D4Science RPrototypingLab VRE https://services.d4science.org/group/rprototypinglab/data-miner?OperatorId = org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.HABITAT_REPRESENTATIVENESS_SCORE (2022).Assante, M. et al. Enacting open science by d4science. Future Generation Computer Systems 101, 555–563 (2019).Article 

Google Scholar 
Assante, M. et al. The gcube system: delivering virtual research environments as-a-service. Future Generation Computer Systems 95, 445–453 (2019).Article 

Google Scholar 
Assante, M. et al. Virtual research environments co-creation: The d4science experience. Concurrency and Computation: Practice and Experience e6925 (2022).Coro, G., Candela, L., Pagano, P., Italiano, A. & Liccardo, L. Parallelizing the execution of native data mining algorithms for computational biology. Concurrency and Computation: Practice and Experience 27, 4630–4644 (2015).Article 

Google Scholar 
Coro, G., Panichi, G., Scarponi, P. & Pagano, P. Cloud computing in a distributed e-infrastructure using the web processing service standard. Concurrency and Computation: Practice and Experience 29, e4219 (2017).Article 

Google Scholar 
Gačić, M., Borzelli, G. E., Civitarese, G., Cardin, V. & Yari, S. Can internal processes sustain reversals of the ocean upper circulation? the ionian sea example. Geophysical research letters 37 (2010).Grilli, F. et al. Seasonal and interannual trends of oceanographic parameters over 40 years in the northern adriatic sea in relation to nutrient loadings using the emodnet chemistry data portal. Water 12, 2280 (2020).Article 

Google Scholar 
Cozzi, S. et al. Climatic and anthropogenic impacts on environmental conditions and phytoplankton community in the gulf of trieste (northern adriatic sea). Water 12, 2652 (2020).Article 

Google Scholar 
Ducrotoy, J.-P. & Elliott, M. The science and management of the north sea and the baltic sea: Natural history, present threats and future challenges. Marine pollution bulletin 57, 8–21 (2008).Article 

Google Scholar 
Dupont, N. & Aksnes, D. L. Centennial changes in water clarity of the baltic sea and the north sea. Estuarine, Coastal and Shelf Science 131, 282–289 (2013).Article 
ADS 

Google Scholar 
Dippner, J. W., Möller, C. & Hänninen, J. Regime shifts in north sea and baltic sea: a comparison. Journal of Marine Systems 105, 115–122 (2012).Article 
ADS 

Google Scholar 
Sisma-Ventura, G. et al. Post-eastern mediterranean transient oxygen decline in the deep waters of the southeast mediterranean sea supports weakening of ventilation rates. Frontiers in Marine Science 1202 (2021).Mavropoulou, A.-M., Vervatis, V. & Sofianos, S. Dissolved oxygen variability in the mediterranean sea. Journal of Marine Systems 208, 103348 (2020).Article 

Google Scholar 
Tyberghein, L. et al. Bio-oracle: a global environmental dataset for marine species distribution modelling. Global ecology and biogeography 21, 272–281 (2012).Article 

Google Scholar 
Assis, J. et al. Bio-oracle v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography 27, 277–284 (2018).Article 

Google Scholar 
Coro, G. & Bove, P. A high-resolution global-scale model for covid-19 infection rate. ACM Transactions on Spatial Algorithms and Systems (TSAS) 8, 1–24 (2022).Article 

Google Scholar 
Inness, A. et al. The cams reanalysis of atmospheric composition. Atmospheric Chemistry and Physics 19, 3515–3556 (2019).Article 
ADS 

Google Scholar 
Karger, D. N., Schmatz, D. R., Dettling, G. & Zimmermann, N. E. High-resolution monthly precipitation and temperature time series from 2006 to 2100. Scientific data 7, 1–10 (2020).Article 

Google Scholar 
Kesner-Reyes, K. et al. AquaMaps Environmental Dataset: Half-Degree Cells Authority File (HCAF ver. 7, 10/2019). AquaMaps Web site https://www.aquamaps.org/main/envt_data.php (2019).Kesner-Reyes, K. et al. AquaMaps Environmental Dataset: Half-Degree Cells Authority File (HCAF ver. 6, 08/2016). AquaMaps Web site https://www.aquamaps.org/main/envt_data.php (2016).NASA-NEX. NASA Earth Exchange data. NASA-NEX Web site https://www.nasa.gov/nex/data – data were publicly accessible up to 2020 (2020).Coro, G. A global-scale ecological niche model to predict sars-cov-2 coronavirus infection rate. Ecological modelling 431, 109187 (2020).Article 

Google Scholar 
CAMS. Global inversion-optimised greenhouse gas fluxes and concentrations. Copernicus Atmosphere Web site https://ads.atmosphere.copernicus.eu/cdsapp#/dataset/cams-global-greenhouse-gas-inversion?tab=doc (2020).NOAA. ETOPO2 Topography and Bathymetry. NOAA Web site https://sos.noaa.gov/catalog/datasets/etopo2-topography-and-bathymetry-natural-colors/ (2010).Coro, G. & Trumpy, E. Predicting geographical suitability of geothermal power plants. Journal of Cleaner Production 267, 121874 (2020).Article 

Google Scholar 
NOAA. World Vector Shorelines. NOAA Web site https://shoreline.noaa.gov/data/datasheets/wvs.html (2019).Tozer, B. et al. Global bathymetry and topography at 15 arc sec: Srtm15+. Earth and Space Science 6, 1847–1864, https://doi.org/10.1029/2019EA000658 (2019).Article 
ADS 

Google Scholar 
Ramesh, R. et al. Land–ocean interactions in the coastal zone: Past, present & future. Anthropocene 12, 85–98 (2015).Article 

Google Scholar 
Spalding, M. et al. World atlas of coral reefs (Univ of California Press, 2001).Laske, G. A global digital map of sediment thickness. Eos Trans. AGU 78, F483 (1997).
Google Scholar 
Davies, J. H. Global map of solid earth surface heat flow. Geochemistry, Geophysics, Geosystems 14, 4608–4622 (2013).Article 
ADS 

Google Scholar 
Rybach, L. & Muffler, L. J. P. Geothermal systems: principles and case histories. Chichester, Sussex, England and New York, Wiley-Interscience, 1981. 371 p. (1981).Glassley, W. E. Geology and hydrology of geothermal energy. Power Stations Using Locally Available Energy Sources: A Volume in the Encyclopedia of Sustainability Science and Technology Series, Second Edition 23–34 (2018).Barbier, E. Geothermal energy technology and current status: an overview. Renewable and sustainable energy reviews 6, 3–65 (2002).Article 
ADS 

Google Scholar 
Engdahl, E. R., van der Hilst, R. & Buland, R. Global teleseismic earthquake relocation with improved travel times and procedures for depth determination. Bulletin of the Seismological Society of America 88, 722–743 (1998).
Google Scholar 
Engdahl, E. R. Global seismicity: 1900–1999. International handbook of earthquake and engineering seismology 665–690 (2002).Richts, A., Struckmeier, W. F. & Zaepke, M. WHYMAP and the groundwater resources map of the world 1: 25,000,000. In Sustaining groundwater resources, 159–173 (Springer, 2011).Warszawski, L. et al. Center for international earth science information network—ciesin—columbia university.(2016). gridded population of the world, version 4 (gpwv4): Population density. palisades. ny: Nasa socioeconomic data and applications center (sedac). Atlas of Environmental Risks Facing China Under Climate Change 228, https://doi.org/10.7927/h4np22dq (2017). More

Martinez-Garcia, L. B., De Deyn, G. B., Pugnaire, F. I., Kothamasi, D. & van der Heijden, M. G. A. Symbiotic soil fungi enhance ecosystem resilience to climate change. Glob. Chang. Biol. 23, 5228–5236 (2017).Article 
ADS 

Google Scholar 
Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).Article 

Google Scholar 
Cairney, J. W. G. Extramatrical mycelia of ectomycorrhizal fungi as moderators of carbon dynamics in forest soil. Soil Biol. Biochem. 47, 198–208 (2012).Article 
CAS 

Google Scholar 
Milovic, M., Kebert, M. & Orlovic, S. How mycorrhizas can help forests to cope with ongoing climate change? Sumar. List 145, 279–286 (2021).Article 

Google Scholar 
Hawksworth, D. L. & Luecking, R. Fungal diversity revisited: 2.2 to 3.8 million species. Microbiol. Spectr. 5, 5.4.10 (2017).Article 

Google Scholar 
Stork, N. E. How many species of insects and other terrestrial arthropods are there on Earth? Annu. Rev. Entomol. 63, 31–45 (2018).Article 
CAS 

Google Scholar 
Christenhusz, M. J. M. & Byng, J. W. The number of known plants species in the world and its annual increase. Phytotaxa 261, 201–217 (2016).Article 

Google Scholar 
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).Article 
ADS 
CAS 

Google Scholar 
van der Heijden, M. G. A., Martin, F. M., Selosse, M. A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).Article 

Google Scholar 
Braghiere, R. K. et al. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. 14, e2022MS003204 (2022).Article 
ADS 
CAS 

Google Scholar 
Jaouen, G. et al. Fungi of French Guiana gathered in a taxonomic, environmental and molecular dataset. Sci. Data 6, 206 (2019).Article 

Google Scholar 
Beninde, J. et al. CaliPopGen: A genetic and life history database for the fauna and flora of California. Sci. Data 9, 380 (2022).Article 

Google Scholar 
Gyeltshen, C. & Prasad, K. Biodiversity checklists for Bhutan. Biodivers. Data J. 10, e83798 (2022).Article 

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

Google Scholar 
Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).Article 
ADS 

Google Scholar 
Melo, C. D., Walker, C., Freitas, H., Machado, A. C. & Borges, P. A. V. Distribution of arbuscular mycorrhizal fungi (AMF) in Terceira and Sao Miguel Islands (Azores). Biodivers. Data J. 8, e49759 (2020).Article 

Google Scholar 
Ordynets, A. et al. Aphyllophoroid fungi in insular woodlands of eastern Ukraine. Biodivers. Data J. 5, e22426 (2017).Article 

Google Scholar 
Monteiro, M. et al. A database of the global distribution of alien macrofungi. Biodivers. Data J. 8, e51459 (2020).Article 

Google Scholar 
Filippova, N. et al. Yugra State University Biological Collection (Khanty-Mansiysk, Russia): general and digitisation overview. Biodivers. Data J. 10, e77669 (2022).Article 

Google Scholar 
Wu, B. et al. Current insights into fungal species diversity and perspective on naming the environmental DNA sequences of fungi. Mycology 10, 127–140 (2019).Article 

Google Scholar 
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).Article 
CAS 

Google Scholar 
Gorczak, M. et al. 18th Congress of European Mycologists Bioblitz 2019 – naturalists contribute to the knowledge of mycobiota and lichenobiota of Białowieża Primeval Forest. Acta Mycol. 55, 1–26 (2020).
Google Scholar 
Goncalves, S. C., Haelewaters, D., Furci, G. & Mueller, G. M. Include all fungi in biodiversity goals. Science 373, 403–403 (2021).Article 
ADS 

Google Scholar 
Hochkirch, A. et al. A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv. Biol. 35, 502–509 (2021).Article 

Google Scholar 
Allen, E. B. et al. Patterns and regulation of mycorrhizal plant and fungal diversity. Plant Soil 170, 47–62 (1995).Article 
CAS 

Google Scholar 
Mueller, G. M. & Schmit, J. P. Fungal biodiversity: what do we know? What can we predict? Biodivers. Conserv. 16, 1–5 (2007).Article 

Google Scholar 
Waters, D. P. & Lendemer, J. C. The lichens and allied fungi of Mercer County, New Jersey. Opusc. Philolichenum 18, 17–51 (2019).
Google Scholar 
Waters, D. P. & Lendemer, J. C. A revised checklist of the lichenized, lichenicolous and allied fungi of New Jersey. Bartonia, 1–62 (2019).Schwarze, C. A. The parasitic fungi of New Jersey. (New Jersey Agricultural Experiment Stations, 1917).Moose, R. A., Schigel, D., Kirby, L. J. & Shumskaya, M. Dead wood fungi in North America: an insight into research and conservation potential. Nat. Conserv. 32, 1–17 (2019).Article 

Google Scholar 
Hibbett, D. S. et al. A higher-level phylogenetic classification of the Fungi. Mycol. Res. 111, 509–547 (2007).Article 

Google Scholar 
Hibbett, D. The invisible dimension of fungal diversity. Science 351, 1150–1151 (2016).Article 
ADS 
CAS 

Google Scholar 
James, T. Y., Stajich, J. E., Hittinger, C. T. & Rokas, A. Toward a Fully Resolved Fungal Tree of Life. Annu. Rev. Microbiol. 74, 291–313 (2020).Article 
CAS 

Google Scholar 
Braghiere, R. K. et al. Mycorrhizal distributions impact global patterns of carbon and nutrient cycling. Geophys. Res. Lett. 48, e2021GL094514 (2021).Article 
ADS 
CAS 

Google Scholar 
Bonney, R. et al. Citizen science: A developing tool for expanding science knowledge and scientific literacy. Bioscience 59, 977–984 (2009).Article 

Google Scholar 
Van Vliet, K. & Moore, C. Citizen science initiatives: engaging the public and demystifying science. J. Microbiol. Biol. Educ. 17, 13–16 (2016).Article 

Google Scholar 
Feldman, M. J. et al. Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review. PLoS One 16, e0234587 (2021).Article 
CAS 

Google Scholar 
Shumskaya, M. et al. Fungi of parks, forests and reserves of New Jersey (2007–2019). Version 1.4. Sampling event dataset. Kean University https://doi.org/10.15468/7scek4 (2022).Heilmann-Clausen, J. et al. How citizen science boosted primary knowledge on fungal biodiversity in Denmark. Biol. Conserv. 237, 366–372 (2019).Article 

Google Scholar 
GBIF.Org User. NJMA dataset. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.93232n (2022).GBIF.Org User. New Jersey Agaricomycetes. GBIF Occurrence Download. Dataset. GBIF https://doi.org/10.15468/dl.6j6382 (2022).GBIF.Org User. USA Agaricomycetes. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.ncukzy (2022).GBIF.Org User. Global records Agaricomycetes. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.nk54e7 (2022).Meyke, E. When data management meets project management. Biodivers. Inf. Sci. Stand. 3, e37224 (2019).
Google Scholar 
Wieczorek, J. et al. Darwin Core: an evolving community-developed biodiversity data standard. PLoS One 7, e29715 (2012).Article 
ADS 
CAS 

Google Scholar 
Pagad, S., Genovesi, P., Carnevali, L., Schigel, D. & McGeoch, M. A. Data Descriptor: introducing the global register of introduced and invasive species. Sci. Data 5, 170102 (2018).Article 

Google Scholar 
Registry-Migration.Gbif.Org.GBIF Backbone Taxonomy. GBIF Secretariat. https://doi.org/10.15468/39omei (2021).Mesibov, R. Archived websites: A Data Cleaner’s Cookbook (version 3) and all BASHing data blog posts 1–200. Zenodo https://doi.org/10.5281/zenodo.6423347 (2022).Chamberlain, S. A. & Boettiger, C. R Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints 5, e3304v3301 (2017).
Google Scholar 
Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.7.1. Available from https://cran.rproject.org/package=rgbif (2022).Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
Sousa, D. et al. Tree canopies reflect mycorrhizal composition. Geophys. Res. Lett. 48, e2021GL092764 (2021).Article 
ADS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. https://ggplot2.tidyverse.org (2016).Bederson, B. B., Shneiderman, B. & Wattenberg, M. Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Trans. Graph. 21, 833–854 (2002).Article 

Google Scholar 
Simpson, H. J. & Schilling, J. S. Using aggregated field collection data and the novel r package fungarium to investigate fungal fire association. Mycologia 113, 842–855 (2021).Article 

Google Scholar 
Robertson, T. et al. The GBIF Integrated Publishing Toolkit: Facilitating the efficient publishing of biodiversity data on the Internet. PLoS One 9, e102623 (2014).Article 
ADS 

Google Scholar  More

Asada, M. et al. Close relationship of Plasmodium sequences detected from South American pampas deer (Ozotoceros bezoarticus) to Plasmodium spp. in North American white-tailed deer. Int. J. Parasitol. 7, 44–47. https://doi.org/10.1016/j.ijppaw.2018.01.001 (2018).Article 

Google Scholar 
Boundenga, L. et al. Haemosporidian parasites of antelopes and other vertebrates from Gabon, Central Africa. PLoS ONE 11, e0148958. https://doi.org/10.1371/journal.pone.0148958 (2016).Article 
CAS 

Google Scholar 
Martinsen, E. S., Perkins, S. L. & Schall, J. J. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): Evolution of life-history traits and host switches. Mol. Phylogen. Evol. 47, 261–273. https://doi.org/10.1016/j.ympev.2007.11.012 (2008).Article 
CAS 

Google Scholar 
Templeton, T. J. et al. Ungulate malaria parasites. Sci. Rep. 6, 23230. https://doi.org/10.1038/srep23230 (2016).Article 
ADS 
CAS 

Google Scholar 
Templeton, T. J., Martinsen, E., Kaewthamasorn, M. & Kaneko, O. The rediscovery of malaria parasites of ungulates. Parasitology 143, 1501–1508. https://doi.org/10.1017/s0031182016001141 (2016).Article 

Google Scholar 
Bruce, D., Harvey, D., Hamerton, A. E. & Bruce, L. Plasmodium cephalophi, sp. nov. Proc. R. Soc. B. 87, 45–47 (1913).ADS 

Google Scholar 
Sheather, A. L. A malarial parasite in the blood of a buffalo. J. Comp. Pathol. 32, 223–229 (1919).Article 

Google Scholar 
Kandel, R. C. et al. First report of malaria parasites in water buffalo in Nepal. Vet. Parasitol. Reg. Stud. Rep. 18, 100348. https://doi.org/10.1016/j.vprsr.2019.100348 (2019).Article 

Google Scholar 
de Mello, F. & Paes, S. Sur une plasmodiae du sang des chèvres. C. R. Séanc. Soc. Biol 88, 829–830 (1923).
Google Scholar 
Kaewthamasorn, M. et al. Genetic homogeneity of goat malaria parasites in Asia and Africa suggests their expansion with domestic goat host. Sci. Rep. 8, 5827. https://doi.org/10.1038/s41598-018-24048-0 (2018).Article 
ADS 
CAS 

Google Scholar 
Garnham, P. C. & Edeson, J. F. Two new malaria parasites of the Malayan mousedeer. Riv. Malariol. 41, 1–8 (1962).CAS 

Google Scholar 
Garnham, P. C. & Kuttler, K. L. A malaria parasite of the white-tailed deer (Odocoileus virginianus) and its relation with known species of Plasmodium in other ungulates. Proc. R. Soc. Lond. B 206, 395–402. https://doi.org/10.1098/rspb.1980.0003 (1980).Article 
ADS 
CAS 

Google Scholar 
Martinsen, E. et al. Hidden in plain sight: Cryptic and endemic malaria parasites in North American white-tailed deer (Odocoileus virginianus). Sci. Adv. 2, e1501486. https://doi.org/10.1126/sciadv.1501486 (2016).Article 
ADS 
CAS 

Google Scholar 
Rattanarithikul, R. et al. Illustrated keys to the mosquitoes of Thailand. IV. Anopheles. Southeast Asian. Trop. Med. Public Health 37, 1–128 (2006).
Google Scholar 
Walter Reed Biosystematics Unit. Systematic catalogue of Culicidae. http://mosquitocatalog.org (2021).Manguin, S., Garros, C., Dusfour, I., Harbach, R. E. & Coosemans, M. Bionomics, taxonomy, and distribution of the major malaria vector taxa of Anopheles subgenus Cellia in Southeast Asia: An updated review. Infect. Genet. Evol. 8, 489–503. https://doi.org/10.1016/j.meegid.2007.11.004 (2008).Article 
CAS 

Google Scholar 
Brosseau, L. et al. A multiplex PCR assay for the identification of five species of the Anopheles barbirostris complex in Thailand. Parasit. Vectors 12, 223. https://doi.org/10.1186/s13071-019-3494-8 (2019).Article 

Google Scholar 
Paredes-Esquivel, C., Donnelly, M. J., Harbach, R. E. & Townson, H. A molecular phylogeny of mosquitoes in the Anopheles barbirostris Subgroup reveals cryptic species: implications for identification of disease vectors. Mol. Phylogen. Evol. 50, 141–151. https://doi.org/10.1016/j.ympev.2008.10.011 (2009).Article 
CAS 

Google Scholar 
Taai, K. & Harbach, R. E. Systematics of the Anopheles barbirostris species complex (Diptera: Culicidae: Anophelinae) in Thailand. Zool. J. Linn. Soc. 174, 244–264. https://doi.org/10.1111/zoj.12236 (2015).Article 

Google Scholar 
Garros, C., Van Bortel, W., Trung, H. D., Coosemans, M. & Manguin, S. Review of the Minimus Complex of Anopheles, main malaria vector in Southeast Asia: From taxonomic issues to vector control strategies. Trop. Med. Int. Health 11, 102–114. https://doi.org/10.1111/j.1365-3156.2005.01536.x (2006).Article 
CAS 

Google Scholar 
Dahan-Moss, Y. et al. Member species of the Anopheles gambiae complex can be misidentified as Anopheles leesoni. Malar. J. 19, 89. https://doi.org/10.1186/s12936-020-03168-x (2020).Article 
CAS 

Google Scholar 
Van Bortel, W. et al. Confirmation of Anopheles varuna in Vietnam, previously misidentified and mistargeted as the malaria vector Anopheles minimus. Am. J. Trop. Med. Hyg. 65, 729–732. https://doi.org/10.4269/ajtmh.2001.65.729 (2001).Article 

Google Scholar 
Wharton, R. H., Eyles, D. E., Warren, M., Moorhouse, D. E. & Sandosham, A. A. Investigations leading to the identification of members of the Anopheles umbrosus group as the probable vectors of mouse deer malaria. Bull. 29, 357–374 (1963).CAS 

Google Scholar 
Nugraheni, Y. R. et al. Myzorhynchus series of Anopheles mosquitoes as potential vectors of Plasmodium bubalis in Thailand. Sci. Rep. 12, 5747. https://doi.org/10.1038/s41598-022-09686-9 (2022).Article 
ADS 
CAS 

Google Scholar 
Tu, H. L. C. et al. Development of a novel multiplex PCR assay for the detection and differentiation of Plasmodium caprae from Theileria luwenshuni and Babesia spp. in goats. Acta Trop. 220, 105957. https://doi.org/10.1016/j.actatropica.2021.105957 (2021).Article 
CAS 

Google Scholar 
Cywinska, A., Hunter, F. F. & Hebert, P. D. Identifying Canadian mosquito species through DNA barcodes. Med. Vet. Entomol. 20, 413–424. https://doi.org/10.1111/j.1365-2915.2006.00653.x (2006).Article 
CAS 

Google Scholar 
Hebert, P. D., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270, 313–321. https://doi.org/10.1098/rspb.2002.2218 (2003).Article 
CAS 

Google Scholar 
Ogola, E. O., Chepkorir, E., Sang, R. & Tchouassi, D. P. A previously unreported potential malaria vector in a dry ecology of Kenya. Parasit. Vectors 12, 80. https://doi.org/10.1186/s13071-019-3332-z (2019).Article 

Google Scholar 
Maquart, P. O., Fontenille, D., Rahola, N., Yean, S. & Boyer, S. Checklist of the mosquito fauna (Diptera, Culicidae) of Cambodia. Parasite 28, 60. https://doi.org/10.1051/parasite/2021056 (2021).Article 

Google Scholar 
Tainchum, K. et al. Diversity of Anopheles species and trophic behavior of putative malaria vectors in two malaria endemic areas of northwestern Thailand. J. Vector. Ecol. 39, 424–436. https://doi.org/10.1111/jvec.12118 (2014).Article 

Google Scholar 
Vantaux, A. et al. Anopheles ecology, genetics and malaria transmission in northern Cambodia. Sci. Rep. 11, 6458. https://doi.org/10.1038/s41598-021-85628-1 (2021).Article 
ADS 
CAS 

Google Scholar 
Chookaew, S. et al. Anopheles species composition in malaria high-risk areas in Ranong Province. Dis. Control J. 46, 483–493. https://doi.org/10.14456/dcj.2020.45 (2020).Article 

Google Scholar 
Makanga, B. et al. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc. Natl. Acad. Sci. USA. 113, 5329–5334. https://doi.org/10.1073/pnas.1603008113 (2016).Article 
ADS 
CAS 

Google Scholar 
Ariey, F., Gay, F. & Ménard, R. Malaria Control and Elimination Vol. 254 (Springer, 2020).
Google Scholar 
Williams, J. & Pinto, J. Training Manual on Malaria Entomology (Springer, 2012).
Google Scholar 
Rigg, C. A., Hurtado, L. A., Calzada, J. E. & Chaves, L. F. Malaria infection rates in Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a village within a region targeted for malaria elimination in Panamá. Infect. Genet. Evol. 69, 216–223. https://doi.org/10.1016/j.meegid.2019.02.003 (2019).Article 

Google Scholar 
Torres-Cosme, R. et al. Natural malaria infection in anophelines vectors and their incrimination in local malaria transmission in Darién Panama. PLoS ONE 16, e0250059. https://doi.org/10.1371/journal.pone.0250059 (2021).Article 
CAS 

Google Scholar 
Beebe, N. W. & Saul, A. Discrimination of all members of the Anopheles punctulatus complex by polymerase chain reaction-restriction fragment length polymorphism analysis. Am. J. Trop. Med. Hyg. 53, 478–481. https://doi.org/10.4269/ajtmh.1995.53.478 (1995).Article 
CAS 

Google Scholar 
Perkins, S. L. & Schall, J. J. A molecular phylogeny of malarial parasites recovered from cytochrome b gene sequences. J. Parasitol. 88, 972–978. https://doi.org/10.1645/0022-3395(2002)088[0972:AMPOMP]2.0.CO;2 (2002).Article 
CAS 

Google Scholar 
Snounou, G. et al. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol. Biochem. Parasitol. 61, 315–320. https://doi.org/10.1016/0166-6851(93)90077-B (1993).Article 
CAS 

Google Scholar 
Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for windows 95/98/NT. Nucleic. Acids. Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).Article 
CAS 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).Article 
CAS 

Google Scholar 
Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274. https://doi.org/10.1093/molbev/msu300 (2015).Article 
CAS 

Google Scholar 
Ventim, R. et al. Avian malaria infections in western European mosquitoes. Parasitol. Res. 111, 637–645. https://doi.org/10.1007/s00436-012-2880-3 (2012).Article 

Google Scholar  More

Orr, H. A. The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6, 119–127 (2005).Article 
CAS 

Google Scholar 
Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).Article 

Google Scholar 
Exposito-Alonso, M., Burbano, H. A., Bossdorf, O., Nielsen, R. & Weigel, D. Natural selection on the Arabidopsis thaliana genome in present and future climates. Nature 573, 126–129 (2019).Article 
CAS 

Google Scholar 
Han, G., Wang, W. & Dong, Y. Effects of balancing selection and microhabitat temperature variations on heat tolerance of the intertidal black mussel Septifer virgatus. Integr. Zool. 15, 416–427 (2020).Article 

Google Scholar 
Meester, L. D., Stoks, R. & Brans, K. I. Genetic adaptation as a biological buffer against climate change: Potential and limitations. Integr. Zool. 13, 372–391 (2018).Article 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).Article 
CAS 

Google Scholar 
Günter, F. et al. Genotype-environment interactions rule the response of a widespread butterfly to temperature variation. J. Evol. Biol. 33, 920–929 (2020).Article 

Google Scholar 
Lowry, D. B. et al. QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient. Proc. Natl Acad. Sci. USA 116, 12933–12941 (2019).Article 
CAS 

Google Scholar 
Bates, A. E. et al. Biologists ignore ocean weather at their peril. Nature 560, 299–301 (2018).Article 
CAS 

Google Scholar 
Plotkin, J. B. & Kudla, G. Synonymous but not the same: the causes and consequences of codon bias. Nat. Rev. Genet. 12, 32–42 (2011).Article 
CAS 

Google Scholar 
Zhao, F. et al. Genome-wide role of codon usage on transcription and identification of potential regulators. Proc. Natl Acad. Sci. USA 118, e2022590118 (2021).Hanson, G. & Coller, J. Codon optimality, bias and usage in translation and mRNA decay. Nat. Rev. Mol. Cell Bio. 19, 20–30 (2018).Article 
CAS 

Google Scholar 
Chen, S. et al. Codon-resolution analysis reveals a direct and context-dependent impact of individual synonymous mutations on mRNA level. Mol. Biol. Evol. 34, 2944–2958 (2017).Article 
CAS 

Google Scholar 
Wu, Z. et al. Expression level is a major modifier of the fitness landscape of a protein coding gene. Nat. Ecol. Evol. 6, 103–115 (2022).Article 

Google Scholar 
Lebeuf-Taylor, E., McCloskey, N., Bailey, S. F., Hinz, A. & Kassen, R. The distribution of fitness effects among synonymous mutations in a gene under directional selection. ELife. 8, e45952 (2019).Bailey, S. F., Hinz, A. & Kassen, R. Adaptive synonymous mutations in an experimentally evolved Pseudomonas fluorescens population. Nat. Commun. 5, 4076 (2014).Agashe, D. et al. Large-effect beneficial synonymous mutations mediate rapid and parallel adaptation in a bacterium. Mol. Biol. Evol. 33, 1542–1553 (2016).Article 
CAS 

Google Scholar 
Zhao, Y. et al. Synonymous mutation in growth regulating factor 15 of miR396a target sites enhances photosynthetic efficiency and heat tolerance in poplar. J. Exp. Bot. 72, 4502–4519 (2021).Article 
CAS 

Google Scholar 
Somero, G. N. The physiology of global change: linking patterns to mechanisms. Annu. Rev. Mar. Sci. 4, 39–61 (2012).Article 

Google Scholar 
Helmuth, B. et al. Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015–1017 (2002).Article 
CAS 

Google Scholar 
Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annu Rev. Ecol. Evol. Syst. 37, 373–404 (2006).Article 

Google Scholar 
Seabra, R., Wethey, D. S., Santos, A. M. & Lima, F. P. Side matters: microhabitat influence on intertidal heat stress over a large geographical scale. J. Exp. Mar. Biol. Ecol. 400, 200–208 (2011).Article 

Google Scholar 
Schmidt, P. S. & Rand, D. M. Intertidal microhabitat and selection at MPI: interlocus contrasts in the Northern Acorn Barnacle, Semibalanus balanoides. Evolution 53, 135 (1999).
Google Scholar 
Li, X., Tan, Y., Sun, Y., Wang, J. & Dong, Y. Microhabitat temperature variation combines with physiological variation to enhance thermal resilience of the intertidal mussel Mytilisepta virgata. Funct. Ecol. 35, 2497–2507 (2021).Article 
CAS 

Google Scholar 
Dong, Y. et al. Untangling the roles of microclimate, behaviour and physiological polymorphism in governing vulnerability of intertidal snails to heat stress. Proc. Royal. Soc. B. 284, (2017).Li, X. & Dong, Y. Living on the upper intertidal mudflat: different behavioral and physiological responses to high temperature between two sympatric Cerithidea snails with divergent habitat-use strategies. Mar. Environ. Res. 159, 105015 (2020).Article 
CAS 

Google Scholar 
Wang, J., Peng, X. & Dong, Y. High abundance and reproductive output of an intertidal limpet (Siphonaria japonica) in environments with high thermal predictability. Mar. Life. Sci. Tech. 2, 324–333 (2020).Article 

Google Scholar 
Dong, Y., Liao, M., Han, G. & Somero, G. N. An integrated, multi-level analysis of thermal effects on intertidal molluscs for understanding species distribution patterns. Biol. Rev. 97, 554–581 (2022).Article 

Google Scholar 
Georges, A., Gros, P. & Fodil, N. USP15: a review of its implication in immune and inflammatory processes and tumor progression. Genes Immun. 22, 12–23 (2021).Article 
CAS 

Google Scholar 
Vlasschaert, C., Xia, X., Coulombe, J. & Gray, D. A. Evolution of the highly networked deubiquitinating enzymes USP4, USP15, and USP11. BMC Evol. Biol. 15, 230 (2015).Mallard, F., Nolte, V., Tobler, R., Kapun, M. & Schlötterer, C. A simple genetic basis of adaptation to a novel thermal environment results in complex metabolic rewiring in Drosophila. Genome Biol. 19, 119 (2018).Cornelissen, T. et al. The deubiquitinase USP15 antagonizes Parkin-mediated mitochondrial ubiquitination and mitophagy. Hum. Mol. Genet. 23, 5227–5242 (2014).Article 
CAS 

Google Scholar 
Morton, B. The biology and functional morphology of Septifer bilocularis and Mytilisepta virgata (Bivalvia: Mytiloidea) from corals and the exposed rocky shores, respectively, of Hong Kong. Reg. Stud. Mar. Sci. 235, 485–500 (1995).
Google Scholar 
Boroda, A. V., Kipryushina, Y. O. & Odintsova, N. A. The effects of cold stress on Mytilus species in the natural environment. Cell Stress Chaperones 25, 821–832 (2020).Article 
CAS 

Google Scholar 
Thayer, C. W. Brachiopods versus mussels: competition, predation, and palatability. Science 228, 1527–1528 (1985).Article 
CAS 

Google Scholar 
Iorio, R., Celenza, G. & Petricca, S. Mitophagy: molecular mechanisms, new concepts on Parkin activation and the emerging role of AMPK/ULK1 Axis. Cells 11, 30 (2022).Article 
CAS 

Google Scholar 
Feidantsis, K. et al. Correlation between intermediary metabolism, Hsp gene expression, and oxidative stress-related proteins in long-term thermal-stressed Mytilus galloprovincialis. Am. J. Physiol. Regul. Integr. Comp. Physiol. 319, R264–R281 (2020).Article 
CAS 

Google Scholar 
Heise, K., Puntarulo, S., Portner, H. O. & Abele, D. Production of reactive oxygen species by isolated mitochondria of the Antarctic bivalve Laternula elliptica (King and Broderip) under heat stress. Comp. Biochem. Physiol. C. Toxicol. Pharmacol. 134, 79–90 (2003).Article 
CAS 

Google Scholar 
Abele, D., Heise, K., Portner, H. O. & Puntarulo, S. Temperature-dependence of mitochondrial function and production of reactive oxygen species in the intertidal mud clam Mya arenaria. J. Exp. Biol. 205, 1831–1841 (2002).Article 
CAS 

Google Scholar 
Xiao, Q. et al. Transcriptome analysis reveals the molecular mechanisms of heterosis on thermal resistance in hybrid abalone. BMC Genom. 22, 650 (2021).Li, L. et al. Heat stress induces apoptosis through a Ca2+-mediated mitochondrial apoptotic pathway in human umbilical vein endothelial cells. PLoS ONE 9, e111083 (2014).Article 

Google Scholar 
Gu, Z. T. et al. Heat stress induced apoptosis is triggered by transcription-independent p53, Ca2+ dyshomeostasis and the subsequent Bax mitochondrial translocation. Sci. Rep. 5, 11497 (2015).Article 
CAS 

Google Scholar 
Gerdol, M., De Moro, G., Venier, P. & Pallavicini, A. Analysis of synonymous codon usage patterns in sixty-four different bivalve species. Peer J. 3, e1520 (2015).Article 

Google Scholar 
Zhou, M. et al. Non-optimal codon usage affects expression, structure and function of clock protein FRQ. Nature 495, 111–115 (2013).Article 
CAS 

Google Scholar 
Yu, C. et al. Codon usage influences the local rate of translation elongation to tegulate co-translational protein folding. Mol. Cell 59, 744–754 (2015).Article 
CAS 

Google Scholar 
Spencer, P. S., Siller, E., Anderson, J. F. & Barral, J. M. Silent substitutions predictably alter translation elongation rates and protein folding efficiencies. J. Mol. Biol. 422, 328–335 (2012).Article 
CAS 

Google Scholar 
Pechmann, S., Chartron, J. W. & Frydman, J. Local slowdown of translation by nonoptimal codons promotes nascent-chain recognition by SRP in vivo. Nat. Struct. Mol. Biol. 21, 1100–1105 (2014).Article 
CAS 

Google Scholar 
Kimchi-Sarfaty, C. et al. A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 315, 525–528 (2007).Article 
CAS 

Google Scholar 
Zhou, Z. et al. Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc. Natl Acad. Sci. USA 113, E6117–E6125 (2016).Article 
CAS 

Google Scholar 
Shabalina, S. A., Spiridonov, N. A. & Kashina, A. Sounds of silence: synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Res. 41, 2073–2094 (2013).Article 
CAS 

Google Scholar 
Liao, M., Dong, Y. & Somero, G. N. Thermal adaptation of mRNA secondary structure: stability versus lability. Proc. Natl Acad. Sci. USA 118, e2113324118 (2021).Article 
CAS 

Google Scholar 
Wan, Y. et al. Genome-wide measurement of RNA folding energies. Mol. Cell. 48, 169–181 (2012).Article 

Google Scholar 
Seffens, W. & Digby, D. mRNAs have greater negative folding free energies than shuffled or codon choice randomized sequences. Nucleic Acids Res. 27, 1578–1584 (1999).Article 
CAS 

Google Scholar 
Faure, G., Ogurtsov, A. Y., Shabalina, S. A. & Koonin, E. V. Role of mRNA structure in the control of protein folding. Nucleic Acids Res. 44, 10898–10911 (2016).Article 
CAS 

Google Scholar 
Victor, M. P., Acharya, D., Begum, T. & Ghosh, T. C. The optimization of mRNA expression level by its intrinsic properties-Insights from codon usage pattern and structural stability of mRNA. Genomics 111, 1292–1297 (2019).Article 
CAS 

Google Scholar 
Backlund, M. & Kulozik, A. E. Differential analysis of the nuclear and the cytoplasmic RNA interactomes in living cells. Methods Mol. Biol. 2428, 291–304 (2022).Article 

Google Scholar 
Zaghlool, A. et al. Characterization of the nuclear and cytosolic transcriptomes in human brain tissue reveals new insights into the subcellular distribution of RNA transcripts. Sci. Rep. 11, 4076 (2021).Clark, M. S. et al. Life in the intertidal: cellular responses, methylation and epigenetics. Funct. Ecol. 32, 1982–1994 (2018).Article 

Google Scholar 
Jeremias, G. et al. Synthesizing the role of epigenetics in the response and adaptation of species to climate change in freshwater ecosystems. Mol. Ecol. 27, 2790–2806 (2018).Article 

Google Scholar 
Li, L. et al. Divergence and plasticity shape adaptive potential of the Pacific oyster. Nat. Ecol. Evol. 2, 1751–1760 (2018).Article 

Google Scholar 
Chu, D. & Wei, L. Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation. BMC Cancer. 19, 359 (2019).Lima, F. P. & Wethey, D. S. Robolimpets: measuring intertidal body temperatures using biomimetic loggers. Limol. Oceanogr. Methods 7, 347–353 (2009).Article 

Google Scholar 
Dong, Y. & Williams, G. A. Variations in cardiac performance and heat shock protein expression to thermal stress in two differently zoned limpets on a tropical rocky shore. Mar. Biol. 158, 1223–1231 (2011).Article 

Google Scholar 
Vito, M. Segmented: An R Package to fit regression models with broken-line relationships. R. N. 8, 20–25 (2008).
Google Scholar 
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2021).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).Article 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience. 10, giab008 (2021).Rochette, N. C., Rivera Colón, A. G. & Catchen, J. M. Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).Article 
CAS 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).Article 
CAS 

Google Scholar 
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).Article 

Google Scholar 
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).Article 
CAS 

Google Scholar 
Hao, Z. et al. RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput. Sci. 6, e251 (2020).Article 

Google Scholar 
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).Article 
CAS 

Google Scholar 
Letunic, I., Khedkar, S. & Bork, P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res. 49, D458–D460 (2021).Article 
CAS 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).Article 
CAS 

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).Article 
CAS 

Google Scholar 
Mathews, D. H. et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. USA 101, 7287–7292 (2004).Article 
CAS 

Google Scholar 
Mathews, D. H., Sabina, J., Zuker, M. & Turner, D. H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911–940 (1999).Article 
CAS 

Google Scholar 
Moyen, N. E., Somero, G. N. & Denny, M. W. Mussels’ acclimatization to high, variable temperatures is lost slowly upon transfer to benign conditions. J. Exp. Biol. 223, Pt 13 (2020).
Google Scholar 
Havird, J. C. et al. Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: why methodology matters. Funct. Ecol. 34, 1015–1028 (2020).Article 

Google Scholar 
Panova, M. et al. DNA extraction protocols for whole-genome sequencing in marine organisms. Methods Mol. Biol. 1452, 13–44 (2016).Article 
CAS 

Google Scholar 
Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).Article 
CAS 

Google Scholar 
Gerdol, M. et al. The purplish bifurcate mussel Mytilisepta virgata gene expression atlas reveals a remarkable tissue functional specialization. BMC Genomics. 18, 590 (2017).Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods 25, 402–408 (2001).Article 
CAS 

Google Scholar  More

Cheung, W. W. L. et al. Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat. Clim. Chang. 3, 254–258, https://doi.org/10.1038/nclimate1691 (2013).Article 
ADS 

Google Scholar 
Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368, https://doi.org/10.1038/nature12156 (2013).Article 
ADS 
CAS 

Google Scholar 
Hilborn, R. et al. Global status of groundfish stocks. Fish Fish. 00, 1–18, https://doi.org/10.1111/faf.12560 (2021).Article 

Google Scholar 
Aguzzi, J. et al. Challenges to the assessment of benthic populations and biodiversity as a result of rhythmic behaviour: video solutions from cabled observatories. Oceanography and Marine Biology: An Annual Review 50, 233–284 (2012).
Google Scholar 
Aguzzi, J. et al. Coastal observatories for monitoring of fish behaviour and their responses to environmental changes. Reviews in fish biology and fisheries 25, 463–483, https://doi.org/10.1007/s11160-015-9387-9 (2015).Article 

Google Scholar 
Doya, C. et al. Diel behavioral rhythms in sablefish (Anoplopoma fimbria) and other benthic species, as recorded by the Deep-sea cabled observatories in Barkley canyon (NEPTUNE-Canada). Journal of Marine Systems 130, 69–78, https://doi.org/10.1016/j.jmarsys.2013.04.003 (2014).Article 
ADS 

Google Scholar 
Aguzzi, J. et al. Ecological video monitoring of Marine Protected Areas by underwater cabled surveillance cameras. Marine Policy 119, 104052, https://doi.org/10.1016/j.marpol.2020.104052 (2020).Article 

Google Scholar 
Milligan, R. J. et al. Evidence for seasonal cycles in deep‐sea fish abundances: A great migration in the deep SE Atlantic? Journal of Animal Ecology 89, 1593–1603, https://doi.org/10.1111/1365-2656.13215 (2020).Article 

Google Scholar 
Hutchingson, G. E. Concluding remarks. Cold Spring Harbor Symp. 22, 415–427, https://doi.org/10.1101/SQB.1957.022.01.039 (1957).Article 

Google Scholar 
Hut, R. A., Kronfeld-Schor, N., Van Der Vinne, V. & De la Iglesia, H. In search of a temporal niche: environmental factors. Progress in brain research 199, 281–304, https://doi.org/10.1016/B978-0-444-59427-3.00017-4 (2012).Article 

Google Scholar 
Aguzzi, J. et al. The hierarchic treatment of marine ecological information from spatial networks of benthic platforms. Sensors 20, 1751, https://doi.org/10.3390/s20061751 (2020).Article 
ADS 

Google Scholar 
Danovaro, R. et al. A new international ecosystem-based strategy for the global deep ocean. Science 355, 452–454, https://doi.org/10.1126/science.aah7178 (2017).Article 
ADS 
CAS 

Google Scholar 
Aguzzi, J. et al. The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment. ICES Journal of Marine Science 77, 2396–2410, https://doi.org/10.1093/icesjms/fsaa169 (2020).Article 

Google Scholar 
Aguzzi, J. et al. New high-tech flexible networks for the monitoring of deep-sea ecosystems. Environmental science and technology 53, 6616–6631, https://doi.org/10.1021/acs.est.9b00409 (2019).Article 
ADS 
CAS 

Google Scholar 
Rountree, R. A. et al. Towards an optimal design for ecosystem-level ocean observatories. In Oceanography and Marine Biology. Taylor and Francis, pp. 79–106 (2020).Aguzzi, J. et al. Developing technological synergies between deep-sea and space research. Elementa: Science of the Anthropocene 10, 00064, https://doi.org/10.1525/elementa.2021.00064 (2022).Article 

Google Scholar 
Aguzzi, J. et al. Multiparametric monitoring of fish activity rhythms in an Atlantic coastal cabled observatory. Journal of Marine Systems 212, 103424, https://doi.org/10.1016/j.jmarsys.2020.103424 (2020).Article 

Google Scholar 
Matabos et al. Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery ‘big data’ processing? Methods in Ecology and Evolution 8, 996–1004, https://doi.org/10.1111/2041-210X.12746 (2017).Article 

Google Scholar 
Zuazo, A. et al. An automated pipeline for image processing and data treatment to track activity rhythms of Paragorgia arborea in relation to hydrographic conditions. Sensors 20, 6281, https://doi.org/10.3390/s20216281 (2020).Article 
ADS 

Google Scholar 
Dibattista, J. D. et al. Community-based citizen science projects can support the distributional monitoring of fishes. Aquatic Conservation: Marine and Freshwater Ecosystems 31, 3580–3593, https://doi.org/10.1002/aqc.3726 (2021).Article 

Google Scholar 
Malde, K., Handegard, N. O., Eikvil, L. & Salberg, A. B. Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science 77, 1274–1285, https://doi.org/10.1093/icesjms/fsz057 (2020).Article 

Google Scholar 
European Marine Board. Big Data in Marine Science. European Marine Broad Advencing Seas & Ocean Science. https://www.marineboard.eu/publications/big-data-marine-science (2020).Aguzzi, J. et al. The new SEAfloor OBservatory (OBSEA) for remote and long-term coastal ecosystem monitoring. Sensors-Basel 11, 5850–5872, https://doi.org/10.3390/s110605850 (2011).Article 
ADS 

Google Scholar 
Del Rio, J. et al. Obsea: a decadal balance for a cabled observatory deployment. IEEE Access 8, 33163–33177, https://doi.org/10.1109/ACCESS.2020.2973771 (2020).Article 

Google Scholar 
Condal, F. et al. Seasonal rhythm in a Mediterranean coastal fish community as monitored by a cabled observatory. Marine Biology 159, 2809–2817, https://doi.org/10.1007/s00227-012-2041-3 (2012).Article 

Google Scholar 
Naylor, E. Chronobiology of marine organisms (Cambridge University Press, 2010).Weis, J. S., Smith, G., Zhou, T., Santiago-Bass, C. & Weis, P. Effects of contaminants on behavior: biochemical mechanisms and ecological consequences: killifish from a contaminated site are slow to capture prey and escape predators; altered neurotransmitters and thyroid may be responsible for this behavior, which may produce population changes in the fish and their major prey, the grass shrimp. Bioscience 51, 209–217 https://doi.org/10.1641/0006-3568(2001)051[0209:EOCOBB]2.0.CO;2 (2001).Bellido, J. M. et al. Identifying essential fish habitat for small pelagic species in Spanish Mediterranean waters. In Essential Fish Habitat Mapping in the Mediterranean. Springer Netherlands, 171–184 https://doi.org/10.1007/978-1-4020-9141-4_13 (2008).Brander, K. Impacts of climate change on fisheries. Journal of Marine Systems 79, 389–402, https://doi.org/10.1016/j.jmarsys.2008.12.015 (2010).Article 
ADS 

Google Scholar 
Viehman, H. A. & Zydlewski, G. B. Multi-scale temporal patterns in fish presence in a high-velocity tidal channel. PLoS One 12, e0176405, https://doi.org/10.1371/journal.pone.0176405 (2017).Article 
CAS 

Google Scholar 
Van Der Walt, K. A., Porri, F., Potts, W. M., Duncan, M. I. & James, N. C. Thermal tolerance, safety margins and vulnerability of coastal species: Projected impact of climate change induced cold water variability in a temperate African region. Marine Environmental Research 169, 105346, https://doi.org/10.1016/j.marenvres.2021.105346 (2021).Article 
CAS 

Google Scholar 
Marini, S. et al. Tracking fish abundance by underwater image recognition. Scientific reports 8, 1–12, https://doi.org/10.1038/s41598-018-32089-8 (2018).Article 
ADS 
CAS 

Google Scholar 
Sbragaglia, V. et al. Annual rhythms of temporal niche partitioning in the Sparidae family are correlated to different environmental variables. Scientific reports 9, 1–11, https://doi.org/10.1038/s41598-018-37954-0 (2019).Article 
ADS 
CAS 

Google Scholar 
Francescangeli, M. et al. Long-Term Monitoring of Diel and Seasonal Rhythm of Dentex dentex at an Artificial Reef. Frontier in Marine Science 9, 1–17, https://doi.org/10.3389/fmars.2022.801033 (2022).Article 

Google Scholar 
Knausgård, K. M. et al. Temperate fish detection and classification: a deep learning based approach. Applied Intelligence 52, 6988–7001, https://doi.org/10.1007/s10489-020-02154-9 (2022).Article 

Google Scholar 
Wu, J. et al. Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise. ACM Computing Surveys (CSUR) 53, 1–35, https://doi.org/10.1145/3379504 (2020).Article 

Google Scholar 
He J., Mao R., Shao Z. & Zhu F. Incremental Learning in Online Scenario. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13923–13932 https://doi.org/10.1109/CVPR42600.2020.01394 (2020).Zhou, D. W., Yang, Y., & Zhan, D. C. Learning to Classify with Incremental New Class. In IEEE Transactions on Neural Networks and Learning Systems https://doi.org/10.1109/TNNLS.2021.3104882 (2021).Hashmani, M. A., Jameel, S. M., Alhussain, H., Rehman, M. & Budiman, A. Accuracy performance degradation in image classification models due to concept drift. International Journal of Advanced Computer Science and Applications 10, 422–425, https://doi.org/10.14569/ijacsa.2019.0100552 (2019).Article 

Google Scholar 
Langenkämper, D., van Kevelaer, R., Purser, A. & Nattkemper, T. W. Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Front. Mar. Sci. 7, 506, https://doi.org/10.3389/fmars.2020.00506 (2020).Article 

Google Scholar 
Kloster, M., Langenkämper, D., Zurowietz, M., Beszteri, B. & Nattkemper, T. W. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports 10, 1–13, https://doi.org/10.1038/s41598-020-71165-w (2020).Article 
CAS 

Google Scholar 
Ottaviani, E. et al. Assessing the image concept drift at the OBSEA coastal underwater cabled observatory. Frontiers in Marine Science 9, 1–13, https://doi.org/10.3389/fmars.2022.840088 (2022).Article 

Google Scholar 
Katija, K. et al. FathomNet: A global image database for enabling artificial intelligence in the ocean. Scientific reports 12, 1–14, https://doi.org/10.1038/s41598-022-19939-2 (2022).Article 
ADS 
CAS 

Google Scholar 
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence 14, 1137–1145 (1995).
Google Scholar 
Tharwat, A. Classification assessment methods. Applied Computing and Informatics 17, 168–192, https://doi.org/10.1016/j.aci.2018.08.003 (2018).Article 

Google Scholar 
Qi, C., Diao, J. & Qiu, L. On estimating model in feature selection with cross-validation. IEEE Access 7, 33454–33463, https://doi.org/10.1109/ACCESS.2019.2892062 (2019).Article 

Google Scholar 
Lopez-Vazquez, V. et al. Video image enhancement and machine learning pipeline for underwater animal detection and classification at cabled observatories. Sensors 20, 726, https://doi.org/10.3390/s20030726 (2020).Article 
ADS 

Google Scholar 
Francescangeli, M. et al. Underwater camera photos with manual tagging of fish species at OBSEA seafloor observatory from 2013 to 2014. PANGAEA https://doi.pangaea.de/10.1594/PANGAEA.946149 (2022).Marini, S. Source code for: simoneMarinIsmar/Image-Tagging-tool: Image Tagging (v1.0). Zenodo https://doi.org/10.5281/zenodo.6566282 (2022).Froese, R. & Pauly, D. FishBase. www.fishbase.org (2019).Martinez Padro, E. et al. CTD data acquired at the OBSEA seafloor observatory from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.946015 (2022).Martinez Padro, E. et al. Meteorological data from a weather station at Vilanova i la Geltrú (Catalonia, Spain) from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.945911 (2022).Martinez Padro, E. et al. Meteorological data from a weather station at Sant Pere de Ribes (Catalonia, Spain) from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.945906 (2022).Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788 https://doi.org/10.1109/CVPR.2016.91 (2016).Marrable, D. et al. Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning. Frontiers in Marine Science 9, 944582, https://doi.org/10.3389/fmars.2022.944582 (2022).Article 

Google Scholar 
Zabala, M., García-Rubies, A., & Corbera, J. Els peixos de les illes Medes i del litoral català: guia per observar-los al seu ambient (Centre d’Estudis Marins de Badalona, 1992).Corbera, J., Sabatés, A., & García-Rubies, A. Peces de mar de la península ibérica (Ed. Planeta, 1996).Mercader, L., Lloris, D., & Rucabado, J. Tots els peixos del mar Català: Diagnosis i claus d’identificació (Institut d’Estudis Catalans, 2001).Aguzzi, J. et al. Daily activity rhythms in temperate coastal fishes: insights from cabled observatory video monitoring. Marine Ecology Progress Series 486, 223–236, https://doi.org/10.3354/meps10399 (2013).Article 
ADS 

Google Scholar 
Campos‐Candela, A. et al. A camera‐based method for estimating absolute density in animals displaying home range behaviour. Journal of Animal Ecology 87, 825–837, https://doi.org/10.1111/1365-2656.12787 (2018).Article 

Google Scholar 
Jang, J. & Yoon, S. Feature concentration for supervised and semisupervised learning with unbalanced datasets in visual inspection. IEEE Transactions on Industrial Electronics 68, 7620–7630, https://doi.org/10.1109/TIE.2020.3003622 (2020).Article 

Google Scholar 
Zhang, J. et al. Adaptive Vertical Federated Learning on Unbalanced Features. IEEE Transactions on Parallel and Distributed Systems 33, 4006–4018, https://doi.org/10.1109/TPDS.2022.3178443 (2022).Article 

Google Scholar 
Lin, C. H., Lin, C. S., Chou, P. Y. & Hsu, C. C. An Efficient Data Augmentation Network for Out-of-Distribution Image Detection. IEEE Access 9, 35313–35323, https://doi.org/10.1109/ACCESS.2021.3062187 (2021).Article 

Google Scholar 
Lu, Y., Chen, D., Olaniyi, E. & Huang, Y. Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review. Computers and Electronics in Agriculture 200, 107208, https://doi.org/10.1016/j.compag.2022.107208 (2022).Article 

Google Scholar 
Waqas, N., Safie, S. I., Kadir, K. A., Khan, S. & Khel, M. H. K. DEEPFAKE Image Synthesis for Data Augmentation. IEEE Access 10, 80847–80857, https://doi.org/10.1109/ACCESS.2022.3193668 (2022).Article 

Google Scholar  More

Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO 2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

Google Scholar 
Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 1–13 (2019).
Google Scholar 
Lovelock, C. E. & Duarte, C. M. Dimensions of Blue Carbon and emerging perspectives. Biol. Lett. 15, 20180781 (2019).Article 

Google Scholar 
Macreadie, P. I. et al. Blue carbon as a natural climate solution. Nat Rev Earth Environ 2, 826–839 (2021).Al‐Haj, A. N. & Fulweiler, R. W. A synthesis of methane emissions from shallow vegetated coastal ecosystems. Glob. Chang. Biol. 26, 2988–3005 (2020).Article 
ADS 

Google Scholar 
Rosentreter, J. A. et al. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nat. Geosci. https://doi.org/10.1038/s41561-021-00715-2 (2021).Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science (80-) 331, 50–50 (2011).Article 
ADS 
CAS 

Google Scholar 
Rosentreter, J. A., Maher, D. T., Erler, D. V., Murray, R. H. & Eyre, B. D. Methane emissions partially offset “blue carbon” burial in mangroves. Sci. Adv. 4, eaao4985 (2018).Article 
ADS 

Google Scholar 
Rosentreter, J. A., Al‐Haj, A. N., Fulweiler, R. W. & Williamson, P. Methane and nitrous oxide emissions complicate coastal blue carbon assessments. Glob. Biogeochem. Cycles 35, e2020GB006858 (2021).Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8 (2005).Article 
ADS 
CAS 

Google Scholar 
Snelgrove, P. V. R. et al. Global carbon cycling on a heterogeneous seafloor. Trends Ecol. Evol. 33, 96–105 (2018).Article 

Google Scholar 
Ortega, A. et al. Important contribution of macroalgae to oceanic carbon sequestration. Nat. Geosci. 12, 748–754 (2019).Article 
ADS 
CAS 

Google Scholar 
Barnes, R. O. & Goldberg, E. D. Methane production and consumption in anoxic marine sediments. Geology 4, 297 (1976).Article 
ADS 
CAS 

Google Scholar 
Reeburgh, W. S. Rates of biogeochemical processes in anoxic sediments. Annu. Rev. Earth Planet. Sci. 11, 269–298 (1983).Article 
ADS 
CAS 

Google Scholar 
Wallenius, A. J., Dalcin Martins, P., Slomp, C. P. & Jetten, M. S. M. Anthropogenic and environmental constraints on the microbial methane cycle in coastal sediments. Front. Microbiol. 12, 631621 (2021).Tokoro, T. et al. Net uptake of atmospheric CO2 by coastal submerged aquatic vegetation. Glob. Chang. Biol. 20, 1873–1884 (2014).Article 
ADS 

Google Scholar 
Gallagher, J. B., Shelamoff, V. & Layton, C. Seaweed ecosystems may not mitigate CO2 emissions. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsac011 (2022).Oremland, R. S. & Taylor, B. F. Sulfate reduction and methanogenesis in marine sediments. Geochim. Cosmochim. Acta 42, 209–214 (1978).Article 
ADS 
CAS 

Google Scholar 
Egger, M., Riedinger, N., Mogollón, J. M. & Jørgensen, B. B. Global diffusive fluxes of methane in marine sediments. Nat. Geosci. 11, 421–425 (2018).Article 
ADS 
CAS 

Google Scholar 
Weber, T., Wiseman, N. A. & Kock, A. Global ocean methane emissions dominated by shallow coastal waters. Nat. Commun. 10, 1–10 (2019).Article 

Google Scholar 
Neubauer, S. C. & Megonigal, J. P. Moving beyond global warming potentials to quantify the climatic role of ecosystems. Ecosystems 18, 1000–1013 (2015).Article 

Google Scholar 
Neubauer, S. C. Global warming potential is not an ecosystem property. Ecosystems https://doi.org/10.1007/s10021-021-00631-x (2021).Howard, J., Hoyt, S., Isensee, K., Telszewski, M. & Pidgeon, E. Coastal blue carbon: methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrasses. 1–181 (2014). https://unesdoc.unesco.org/ark:/48223/pf0000372868.Berg, P., Huettel, M., Glud, R. N., Reimers, C. E. & Attard, K. M. Aquatic eddy covariance: the method and its contributions to defining oxygen and carbon fluxes in marine environments. Ann. Rev. Mar. Sci. 14, 431–455 (2022).Article 

Google Scholar 
Tokoro, T., Watanabe, K., Tada, K. & Kuwae, T. Air–water CO2 flux in shallow coastal waters: theory, methods, and empirical studies. in Blue Carbon in Shallow Coastal Ecosystems 153–184 (Springer Singapore, 2019).Saintilan, N., Rogers, K., Mazumder, D. & Woodroffe, C. Allochthonous and autochthonous contributions to carbon accumulation and carbon store in southeastern Australian coastal wetlands. Estuar. Coast. Shelf Sci. 128, 84–92 (2013).Article 
ADS 
CAS 

Google Scholar 
Ollivier, Q. R., Maher, D. T., Pitfield, C. & Macreadie, P. I. Net drawdown of greenhouse gases (CO2, CH4 and N2O) by a temperate australian seagrass meadow. Estuaries Coasts https://doi.org/10.1007/s12237-022-01068-8 (2022).Maher, D. T. et al. Novel use of cavity ring-down spectroscopy to investigate aquatic carbon cycling from microbial to ecosystem scales. Environ. Sci. Technol. 47, 12938–12945 (2013).Article 
ADS 
CAS 

Google Scholar 
Call, M. et al. Spatial and temporal variability of carbon dioxide and methane fluxes over semi-diurnal and spring–neap–spring timescales in a mangrove creek. Geochim. Cosmochim. Acta 150, 211–225 (2015).Article 
ADS 
CAS 

Google Scholar 
Maher, D. T., Cowley, K., Santos, I. R., Macklin, P. & Eyre, B. D. Methane and carbon dioxide dynamics in a subtropical estuary over a diel cycle: Insights from automated in situ radioactive and stable isotope measurements. Mar. Chem. 168, 69–79 (2015).Article 
CAS 

Google Scholar 
Attard, K. M. et al. Seasonal metabolism and carbon export potential of a key coastal habitat: The perennial canopy-forming macroalga Fucus vesiculosus. Limnol. Oceanogr. 64, 149–164 (2019).Article 
ADS 

Google Scholar 
Attard, K. M. et al. Seasonal ecosystem metabolism across shallow benthic habitats measured by aquatic eddy covariance. Limnol. Oceanogr. Lett. 4, 79–86 (2019).Article 

Google Scholar 
Trevathan-Tackett, S. M. et al. Comparison of marine macrophytes for their contributions to blue carbon sequestration. Ecology 96, 3043–3057 (2015).Article 

Google Scholar 
Pessarrodona, A. et al. Global seaweed productivity. Sci. Adv. 8, eabn2465 (2022).Machado, L., Magnusson, M., Paul, N. A., de Nys, R. & Tomkins, N. Effects of marine and freshwater macroalgae on in vitro total gas and methane production. PLoS ONE 9, e85289 (2014).Article 
ADS 

Google Scholar 
Hansson, G. Methane production from marine, green macro-algae. Resour. Conserv. 8, 185–194 (1983).Article 
CAS 

Google Scholar 
Björk, M., Rosenqvist, G., Gröndahl, F. & Bonaglia, S. Methane emissions from macrophyte beach wrack on Baltic seashores. Ambio 52, 171–181 (2023).Article 

Google Scholar 
Lundevall-Zara, M., Lundevall-Zara, E. & Brüchert, V. Sea-air exchange of methane in shallow inshore areas of the Baltic sea. Front. Mar. Sci. 8, 1–20 (2021).Article 

Google Scholar 
Yvon-Durocher, G. et al. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507, 488–491 (2014).Article 
ADS 
CAS 

Google Scholar 
Roth, F. et al. High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems. Glob. Chang. Biol. https://doi.org/10.1111/gcb.16177 (2022).Koweek, D. A. et al. A year in the life of a central California kelp forest: physical and biological insights into biogeochemical variability. Biogeosciences 14, 31–44 (2017).Article 
ADS 
CAS 

Google Scholar 
Watanabe, K. et al. Macroalgal metabolism and lateral carbon flows can create significant carbon sinks. Biogeosciences 17, 2425–2440 (2020).Article 
ADS 
CAS 

Google Scholar 
Möller, P., Pihl, L. & Rosenberg, R. Benthic faunal energy flow and biological interaction in some shallow marine soft bottom habitats. Mar. Ecol. Prog. Ser. 27, 109–121 (1985).Article 
ADS 

Google Scholar 
Frigstad, H. et al. Blue Carbon – Climate Adaptation, CO2 Uptake And Sequestration Of Carbon In Nordic Blue Forests – Results From The Nordic Blue Carbon Project. (Nordic Council of Ministers, 2021).Ikawa, H. & Oechel, W. C. Temporal variations in air-sea CO 2 exchange near large kelp beds near San Diego, California. J. Geophys. Res. Ocean. 120, 50–63 (2015).Article 
ADS 
CAS 

Google Scholar 
Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
CAS 

Google Scholar 
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).Article 
ADS 
CAS 

Google Scholar 
Oreska, M. P. J. et al. The greenhouse gas offset potential from seagrass restoration. Sci. Rep. 10, 1–15 (2020).Article 

Google Scholar 
Asplund, M. E. et al. Methane emissions from nordic seagrass meadow sediments. Front. Mar. Sci. 8, 811533 (2022).Article 

Google Scholar 
Schorn, S., Ahmerkamp, S., Bullock, E., Weber, M. & Lott, C. Diverse methylotrophic methanogenic archaea cause high methane emissions from seagrass meadows. Proc. Natl Acad. Sci. USA. https://doi.org/10.1073/pnas.2106628119/-/DCSupplemental.Published (2022).Koebsch, F., Glatzel, S. & Jurasinski, G. Vegetation controls methane emissions in a coastal brackish fen. Wetl. Ecol. Manag. 21, 323–337 (2013).Article 
CAS 

Google Scholar 
Sansone, F. J. & Martens, C. S. Methane production from acetate and associated methane fluxes from anoxic coastal sediments. Science (80-). 211, 707–709 (1981).Article 
ADS 
CAS 

Google Scholar 
Egger, M. et al. Rapid sediment accumulation results in high methane effluxes from coastal sediments. PLoS ONE 11, e0161609 (2016).Article 

Google Scholar 
Hamdan, L. J. & Wickland, K. P. Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate. Limnol. Oceanogr. 61, S3–S12 (2016).Article 
ADS 

Google Scholar 
Cai, M. et al. Metatranscriptomics reveals different features of methanogenic archaea among global vegetated coastal ecosystems. Sci. Total Environ. 802, 149848 (2022).Article 
ADS 
CAS 

Google Scholar 
Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science (80-). 350, 434–438 (2015).Article 
ADS 
CAS 

Google Scholar 
Zhang, C.-J., Pan, J., Liu, Y., Duan, C.-H. & Li, M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome 8, 94 (2020).Article 
CAS 

Google Scholar 
Hilt, S., Grossart, H., McGinnis, D. F. & Keppler, F. Potential role of submerged macrophytes for oxic methane production in aquatic ecosystems. Limnol. Oceanogr. https://doi.org/10.1002/lno.12095 (2022).Söllinger, A. & Urich, T. Methylotrophic methanogens everywhere — physiology and ecology of novel players in global methane cycling. Biochem. Soc. Trans. 47, 1895–1907 (2019).Article 

Google Scholar 
Karl, D. M. et al. Aerobic production of methane in the sea. Nat. Geosci. 1, 473–478 (2008).Article 
ADS 
CAS 

Google Scholar 
McGenity, T. J. & Sorokin, D. Y. Handbook of Hydrocarbon and Lipid Microbiology. p. 665–680 (Springer, 2010).Murray, B. C., Pendleton, L., Jenkins, W. A. & Sifleet, S. Green Payments for Blue Carbon Economic Incentives for Protecting Threatened Coastal Habitats (Nicholas Institute for Environmental Policy Solutions, 2011).Kuwae, T., Watanabe, A., Yoshihara, S., Suehiro, F. & Sugimura, Y. Implementation of blue carbon offset crediting for seagrass meadows, macroalgal beds, and macroalgae farming in Japan. Mar. Policy 138, 104996 (2022).Article 

Google Scholar 
Medvedev, I. P., Rabinovich, A. B. & Kulikov, E. A. Tides in three enclosed basins: the Baltic, Black, and Caspian Seas. Front. Mar. Sci. 3, 46 (2016).Article 

Google Scholar 
Haugen, D. A. Workshop on Micrometeorology (American Meteorological Society, 1973).Weiss, R. F. Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Mar. Chem. 2, 203–215 (1974).Article 
CAS 

Google Scholar 
Wiesenburg, D. A. & Guinasso, N. L. Equilibrium solubilities of methane, carbon monoxide, and hydrogen in water and sea water. J. Chem. Eng. Data 24, 356–360 (1979).Article 
CAS 

Google Scholar 
Wanninkhof, R. Relationship between wind speed and gas exchange over the ocean revisited. Limnol. Oceanogr. Methods 12, 351–362 (2014).Article 

Google Scholar 
Gülzow, W. et al. One year of continuous measurements constraining methane emissions from the Baltic Sea to the atmosphere using a ship of opportunity. Biogeosciences 10, 81–99 (2013).Article 
ADS 

Google Scholar 
Jähne, B. et al. On the parameters influencing air-water gas exchange. J. Geophys. Res. 92, 1937 (1987).Article 
ADS 

Google Scholar 
Bonaglia, S. et al. Meiofauna improve oxygenation and accelerate sulfide removal in the seasonally hypoxic seabed. Mar. Environ. Res. 159, 104968 (2020).Article 
CAS 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).Article 
CAS 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

Google Scholar 
St John, J. SeqPrep. https://github.com/jstjohn/SeqPrep (2011).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2021).Andrews, S. FastQC: A Quality Control Tool For High Throughput Sequence Data (2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).Article 
CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41, D590–D596 (2012).Article 

Google Scholar 
Robertson, C. E. et al. Explicet: graphical user interface software for metadata-driven management, analysis and visualization of microbiome data. Bioinformatics 29, 3100–3101 (2013).Article 
CAS 

Google Scholar 
Coolen, M. J. L. et al. Evolution of the methane cycle in Ace Lake (Antarctica) during the Holocene: response of methanogens and methanotrophs to environmental change. Org. Geochem. 35, 1151–1167 (2004).Article 
CAS 

Google Scholar 
Wobbrock, J. O., Findlater, L., Gergle, D. & Higgins, J. J. The aligned rank transform for nonparametric factorial analyses using only anova procedures. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 143–146 (ACM, 2011).Hammer, Ø., Harper, D. & Ryan, P. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001). More