Philippa Kaur

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

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

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

Naturalist Alfred Russel Wallace went on an expedition to Amazonas state in Brazil in 1848–52.Credit: Mondadori Portfolio via Getty

Best known for formulating the theory of evolution by natural selection, independently of Charles Darwin, Alfred Russel Wallace is an appealing if enigmatic figure. The appeal stems in part from his underdog status: poor and self-educated, Wallace had none of Darwin’s social and financial advantages. The enigma comes from his keen embrace of a range of eccentric non-scientific causes, including spiritualism, phrenology and anti-vaccination (for smallpox).Scientists do not like their scientific heroes to bear the taint of irrational thinking. Wallace’s enthusiasms have therefore contributed to him becoming marginalized in the history of evolutionary thought. Most people know about Darwin and the HMS Beagle. But what about Wallace and the Helen?The Helen story is worth revisiting because it shows Wallace at his resolute best. Despite numerous disastrous career setbacks — of which the Helen episode was the most severe — he persevered and eventually succeeded as a scientist.More than 150 years after Wallace’s experience on the Helen, doing science continues to be hard and can be disappointing. Wallace’s misadventure provides both perspective and an object lesson in how to navigate setbacks. His response to problems showcases his most inspiring traits: his commitment to science, his almost superhuman resilience and his refusal to mire himself in self-pity.Tropical explorationsIn his first job as a land surveyor, Wallace developed an interest in the plants he encountered as he tramped across the countryside. Then, in 1844 at the age of 21, he met Henry Walter Bates, who would later discover ‘Batesian mimicry’ (whereby members of a palatable prey species gain protection by mimicking an unpalatable one).Bates, two years Wallace’s junior, had a fixation with beetles, and he catalysed Wallace’s transformation from hobbyist naturalist to serious collector. Wallace’s new-found focus on beetles transcended mere entomological stamp-collecting; he developed an interest in some of the great scientific questions of the time. He was particularly inspired by the anonymously published Vestiges of the Natural History of Creation (1844) by Robert Chambers, which put forward a vision of a transmutational process, with life progressing from simple to complex.Without money or connections, Wallace and Bates aspired to careers in science at a time when the field was the preserve of the moneyed elite. They would have to fund their scientific explorations by collecting and selling specimens. After a hasty choice of destination — tropical South America — and a crash course in collecting methods, Wallace, aged 25, and Bates, aged 23, arrived in Belém, Brazil, in May 1848 (see ‘Doggedly determined’).
Doggedly determined

Alfred Russel Wallace tends to be unjustly relegated to a footnote in the Charles Darwin story. He was, in fact, a pioneering biologist who refused to let disadvantage or disaster prevent him from pursuing his scientific dreams.
January 1823: Alfred Russel Wallace is born in Usk in Wales.
May 1848: Wallace and Henry Walter Bates arrive in Belém, Brazil.
July 1852: Wallace boards the Helen, which catches fire three weeks later while at sea.
October 1852: Wallace reaches Deal, England, aboard the Jordeson.
March 1854: Wallace leaves Southampton for southeast Asia.
September 1855: Wallace’s first evolutionary paper describing his ‘Sarawak Law’ is published.
May 1856: Citing the Sarawak Law paper, geologist Charles Lyell alerts Darwin to the possibility that Wallace is developing ideas similar to Darwin’s.
February 1858: Wallace sends his paper on natural selection to Darwin from Ternate in the Maluku islands (Moluccas), Indonesia.
July 1858: The joint Darwin–Wallace paper is presented at the Linnean Society in London.
November 1859: Darwin’s On the Origin of Species is published.
March 1862: Wallace returns from southeast Asia.
November 1913: Wallace dies in Broadstone, England.

The two split up early on, with Wallace concentrating on the Amazon River’s northern tributary, the Rio Negro, and Bates on the southern fork, the Solimões.Collecting was challenging. The Amazon’s ubiquitous ants often deprived science of hard-won specimens. Crucial collecting materials also disappeared: Wallace once recovered from a bout of fever to discover that local people had drunk the cachaça (a Brazilian rum) he’d been using to pickle specimens. Transport was a constant headache, with travel upstream past rapids requiring unwieldy portages of canoes and cargo. And thanks to his collecting, the cargo became ever more voluminous and unwieldy.Wallace and Bates sporadically sent back shipments of material to their agent in London, Samuel Stevens, who publicized their adventures in scientific journals and sold their specimens, taking a 20% commission.
Escaping Darwin’s shadow: how Alfred Russel Wallace inspires Indigenous researchers
Wallace’s journeys on the Rio Negro and its tributaries took him into areas that had not yet been visited by Europeans. He saw (and collected) an extraordinary array of species, many of them new to science. He had a chance to observe and collect artefacts from several Indigenous groups with little or no previous contact with Europeans. As he travelled, Wallace capitalized on his surveying skills to map the terrain. But the remoteness took its toll. He made an “inward vow never to travel again in such wild, unpeopled districts without some civilised companion or attendant”1.Wallace was frequently ill, on one occasion nearly lethally so. His younger brother came out to join him as an assistant in 1849 but died of yellow fever two years later in Belém, on his way back to England. Wallace learnt that his brother was sick but had to wait many anxious months before news of his death made it upriver.In 1852, after four years of exploring and collecting, it was time for Wallace himself to head home. He envisaged a triumphant return. He would complement his collections of preserved organisms with a menagerie of living ones. Mr Wallace’s biological wonders would surely be the toast of scientific London.On 12 July in Belém, Wallace boarded the Helen, a freighter ship bound for London. The trip across the continent to Belém had not gone smoothly. The authorities in Manaus, Brazil, had had to be persuaded to release some of his earlier shipments meant for London, which they had impounded, making the final haul aboard the Helen even larger. But now all that remained was the long voyage back across the Atlantic. Wallace, who shared Captain Turner’s cabin, was the only passenger.Disaster strikesThree weeks into the voyage, Captain Turner interrupted Wallace’s morning routine to tell him that the ship was on fire.Friction caused by the rocking of the ship had ignited poorly stowed cargo. Attempts to intervene were counterproductive — removing the hold covers merely oxygenated the fire — and soon the ship became what Wallace later called “a most magnificent conflagration”1.Captain Turner gave the order to abandon ship, and the scramble to prepare two small wooden boats began. Having been stored on deck in the tropical sunshine, both boats leaked badly. The cook had to find corks to plug their hulls.Before he left the ship, Wallace “went down into the cabin, now suffocatingly hot and full of smoke, to see what was worth saving”1. He retrieved his “watch and a small tin box containing some shirts and a couple of old note-books, with some drawings of plants and animals, and scrambled up with them on deck”1. He tried to lower himself on a rope into one of the small boats, but fever-weakened, he ended up sliding down the rope, stripping the skin off his hands.

Some of Alfred Russel Wallace’s sketches were salvaged from the fire aboard the Helen on his return journey from South America in 1852.Credit: The Natural History Museum/Alamy

With fine weather, the best hope of rescue lay in other ships seeing the fire. The two boats duly circled the burning wreck for the next 24 hours, meaning that Wallace got to witness every moment of the tragedy. The animals he had brought with him on the long river journey across the continent, now free from their cages, sought refuge on the one part of the ship still untouched by the flames, the bowsprit. Wallace watched as the monkeys, parrots and more — his pets as well as his best hope of impressing London’s scientific elite — were incinerated.The hoped-for rescue did not immediately materialize, and Captain Turner turned the two open boats towards Bermuda, 1,100 kilometres away to the northwest.As the days ticked by, the situation became increasingly desperate. Water ran low and the tropical sun left Wallace’s “hands and face very much blistered”1. Wallace nevertheless remained upbeat, later recalling that during one night, he “saw several meteors, and in fact could not be in a better position for observing them, than lying on [his] back in a small boat in the middle of the Atlantic”1.Finally, ten days into the ordeal, salvation appeared on the horizon in the form of the Jordeson, a creaking and already overladen cargo ship bound for London.With the immediate crisis past, the magnitude of what had happened started to sink in. In a letter2 written aboard the Jordeson to botanist Richard Spruce (see go.nature.com/3prhbdk), Wallace tallied his catastrophic losses — “almost all the reward of my four years of privation & danger was lost” — and concluded with characteristic understatement, “I have some need of philosophic resignation to bear my fate with patience and equanimity.”
Evolution’s red-hot radical
The Jordeson finally limped into Deal, England, on 1 October 1852. Wallace had been at sea for 80 days. His outward voyage with Bates had taken only 29 days.Wallace added a PS to his letter to Spruce. First there was immediate exhilaration about the return — “Such a dinner! Oh! beef steaks & damson tart”. But then came thoughts about the future: “Fifty times since I left Pará [Belém] have I vowed if I once reached England never to trust myself more on the ocean.” Even then, he noted that “good resolutions soon fade”.Stevens had thoughtfully taken out insurance. So Wallace had £200 (US$980 at the time) — a fraction of his collections’ actual value — to cover his costs for a year in London while he tried to salvage what he could from the disaster and make future plans.He rushed out two books, one a travelogue, the other a more technical account of the palm trees of the Amazon. Neither did well — 250 copies remained unsold a decade later from the travel book’s print run of 750. But he was getting his name out there. Stevens, too, had a done a good job of publicizing Wallace’s discoveries while Wallace had been away.Perhaps most crucially, the positive response of the UK Royal Geographical Society to his mapping work of the Rio Negro yielded a free steamship ticket to Singapore.In March 1854, less than 18 months since the Jordeson’s bedraggled arrival at Deal, Wallace departed from Southampton in England for what he would call the “central and controlling incident”2 of his life.Eight more years of perilous travel awaited. So, too, did the discoveries of what came to be known as Wallace’s Line (a boundary between the Asian and Australasian biogeographic regions) and of the theory of evolution by natural selection3,4.The scientific acclaim that greeted Wallace’s return from southeast Asia in 1862 was a just reward both for his contributions and for that phenomenal doggedness — his determination, despite everything, to be a scientist. 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

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In a previous study18, we used bioinformatics tools to analyze the metagenome and the amplicon 16S sequences to gain an insight into microbial diversity in Moroccan lagoons, namely Marchica and Oualidia. 16S rRNA gene classification revealed a high percentage of bacteria in both lagoons. On average, bacteria accounted for 90% of the total prokaryotes in Marchica and ~ 70% in Oualidia. The five phyla that were the most abundant in both lagoons, Marchica and Oualidia, respectively, were Proteobacteria (53.62%, 29.18%), Bacteroidetes (16.46%, 43.49%), Cyanobacteria (0.53%, 34.35%), Verrucomicrobia (1.75%, 15.82%), and Actinobacteria (7.42%, 13.98%). At the genus level, we found that the highest assigned hits were attributed to Synechococcus, which was highly abundant in Marchica (32%) compared to Oualidia (0.07%) in 2014. This amount dropped to 22% in Marchica and 0.04% in Oualidia in 2015. Hence, in this study we performed the analysis of the Synechococcus genus community using oligotyping to investigate their dynamics and understand their co-occurrence and covariation in space and time within fragile ecosystems such as lagoons.We may divide our results into two emerging Synechococcus communities: one dominated in 2014 and the other was less present in 2015, each composed of different cooccurring Synechococcus oligotypes. The abundant Synechococcus community in Marchica in 2014 consisted of clades I, 5.3, III, IV, and VII. These clades are typically found in either warmer or more oligotrophic environments19,20. This result is in accordance with Marchica’s environmental characteristics; it is an oligotrophic ecosystem with high primary production and warmer water in summer21. The community included clades CB5 and WPC1 in Marchica 2014 and 2015 when the number of Synechococcus reads was lower. Strains belonging to the CB5 clade lack phycourobilin (PUB), contain one motile strain22,23, are present in temperate coastal waters and are prevalent in polar/subpolar waters24,25,26. WPC1 strains are observed in open-ocean and near-shore waters1,24,27. Clades IV and I usually co-occur and are more prevalent in cold coastal waters19,28,29,30. Interestingly, Clade III was prominent in Marchica. This clade is known to be motile and restricted to warm, oligotrophic water19,20,30. Although at a smaller read number, clade III was also observed in Oualidia, where the temperature is cooler compared to Marchica. Furthermore, we found that clade III growth has been shown to be severely affected at low temperatures30. Moreover, representatives of both clades I and IV were present in Oualidia in both the summers of 2014 and 2015. Some Synechococcus strains, which are known to prefer cooler water temperatures and salinities, were in higher relative abundance in the waters of Marchica. This result agrees with a previous study showing that Synechococcus isolates of clades I and IV exhibited temperature preferences31. Their growth rates were marginally lower at low temperatures in strains from clades I and IV, which were dominant in temperate regions.Nitrate levels are typically low or undetectable in these lagoons, which allows the persistence of clades that would not typically thrive in coastal waters at other times of the year. In 2014, the nitrate concentration was higher than the average of 10 mg/l, which could be due to increased agricultural activities and wastewater treatment plant effluent21. The decreasing nitrate concentration in Marchica in 2015 could be explained by the newly installed inlet in 2010, which was designed to improve water exchange with the open sea and reduce the amount of suspended matter21. Temperature and salinity have a large effect on nitrate in marine ecosystems32; the highest nitrate degradation rates were observed at 35 °C and at increasing salinity rates. Therefore, we expected to see correlations between salinity, temperature and nitrate concentrations. Interestingly, clades CB5 in Marchica and IV in Oualidia increased in relative abundance in summer 2015 compared to 2014, when the nitrate concentration decreased. Moreover, the Synechococcus microbial community diversity and density are variables depending on the variations in the physical and chemical parameters. These parameters are strongly influenced by the marine waters passing through the artificial inlets, which have an impact on the internal hydrodynamics of both lagoons and hence the distribution and co-occurrence of Synechococcus strains. In addition, anthropogenic activities also have a great influence on Synechococcales population growth and interactions with their viruses33,34.This study revealed some differences between Marchica and Oualidia in identified Synechococcus clades. The Marchica lagoon showed more heterogeneity (clades I, II, III, IV, VII, VIII, 5.3, WPC1, CB5, and IX) than the Oualidia lagoon, where fewer clades were identified (I, III, IV, and VII). There was a clear variation in the pattern of correlation between oligotypes of the same or different clades for both the 2014 and 2015 samplings. Furthermore, we observed complex patterns of co-occurrence among oligotypes; in 2014 (clades I, III, IV, 5.3, VII), and in 2015, we found clades CB5 and WPC1. In Oualidia, values decreased in comparison to Marchica in both 2014 and 2015 summer samplings, following a pattern of co-occurrence, especially for both clades I and IV in both sampling years. Many studies have shown that the relative proportions of cooccurring Synechococcus populations to each other at the clade and subclade levels vary in space and time based on environmental factors such as seasonal temperature fluctuations, nutrient availability and upwelling, circulation patterns, and abundance of other phytoplankton8.We presume that the greater variability in oligotype co-occurrence behavior observed in Marchica Lagoon, especially in the summer of 2014, could be due to the higher abundance and diversity of Synechococcus oligotypes, physico-chemical parameter fluctuations or rehabilitation of the lagoon.Less abundant oligotypes could also be considered potential bioindicators of Synechococcus genetic diversity. Their seasonal occurrence might contribute to changing ecological and biogeochemical characteristics of the marine environment35. The Synechococcus relative abundance count revealed that the Marchica Synechococcus community included the least abundant oligotypes in 2015. For instance, O7 and O8 were detected in 2014 and were absent in 2015 (Table 1). It is unclear which factors served to constrain the relative abundances of these least present oligotypes, but temperature and salinity could have an impact on their distribution in Marchica (Fig. 4) and the opposite for Oualidia, which are cooler-temperature adapted ones. We noticed that the relative abundance of cooccurring Synechococcus was not constant. For instance, oligotype 4 belonging to Clade IV showed higher values in summer 2014 (974 reads) in Marchica compared to summer 2015 (319 reads), and the opposite was observed in Oualidia, with a lower abundance compared to Marchica. Increased values of cooccurring clade I oligotypes (14, 26, and 6) were detected in the summer of 2014 in both lagoons.Figure 4Principle component analysis of Synechococcus oligotype relative abundance. The plot is generated using the relative abundance of each oligotype, T temperature, S Salinity, and NO3− Nitrate. Each point represents an oligotype. Colors represent the year of sampling; red for 2014 and blue for 2015. The shape of point indicates the sampling site; rounded points refer to Marchica lagoon, and triangles refer to Oualidia. Circles represent the normal distribution of oligotypes; the red circle refers to 2014, and the blue one refers to 2015.Full size imageIn comparing our results with a study from Little Sippewissett Marsh (LSM)8 that used oligotyping to investigate the distribution of the genus Synechococcus in space and time sequencing the V4-V6 hypervariable region of the 16S rRNA gene, we found 31 oligotypes, while they identified 12. In both studies, the proportion of Synechococcus oligotypes increased in summer and in coastal waters compared to estuaries. In addition, Clades I and IV were more abundant in saline conditions, such as Marchica Lagoon. However, these clades were found in greater relative abundances at cold temperatures, in contrast to our study, where they were identified in Marchica’s warm waters. Moreover, clade CB5 tended to be prominent at relatively warm temperatures (17–20 °C)6. In our work, it was not prevalent either in cooler or warmer water. Notably, the relative abundance of rare oligotypes was higher in warm hypersaline estuary waters8,18, while in our case study, they occurred in cooler moderately saline Oualidia waters.The dominance of a certain clade could have many different ecological ramifications, especially as the clades can be incredibly diverse in their growth, loss, nutrient utilization and other attributes. The dominant clade’s growth and loss patterns will set the stage for the population dynamics. For instance, if the dominant clade only blooms in a given environmental factor such as temperature, light, or salinity, it will then affect the timing of blooms, and follow-on the effects of subsequent grazing, lysis or even biogeochemical cycling. Even if the population is diverse, the dynamics as a whole will be a composite response of each individual clade’s ecophysiology, making it important to understand their composition and how it changes over space and time.While the rpoC1 gene is a higher resolution diversity marker36, 16S amplicon data can be used for exploring the entire bacterial assemblage including Synechococcus clade designations via oligotyping35. The latter has a great advantage in answering unexplained diversity contained in taxa using 16S rRNA gene sequences. Nevertheless, it has some limitations, as it acts optimally only when performed on taxa that are closely related. Regarding distantly related taxa, the high number of increased-entropy locations makes the supervision steps difficult. In addition, although oligotyping does not rely on clustering conditions or availability of existing reads within reference databases, it demands preliminary operational taxonomic unit clustering to find closely related species appropriate for the analysis. This method is under continuous improvement to better exploit the information within subtle variations in 16S rRNA gene sequences5.In conclusion, we explored the patterns of Synechococcus diversity in space and time using an oligotyping approach to examine these populations in lagoon waters of Mediterranean Marchica and Atlantic Oualidia, in Morocco. Patterns that have been observed at the clade and subclade levels, such as Synechococcus, relative abundance and the co-occurrence of groups from different clades, were shown to occur among oligotypes. The Marchica Lagoon showed a heterogeneous Synechococcus diversity compared to Oualidia in summer 2014. Thirty-one Synechococcus oligotypes were identified. Two distinct communities emerged in the 2014 and 2015 summer samplings, abundant and rare Synechococcus species, each comprising cooccurring Synechococcus oligotypes from different clades. Network analysis showed that six oligotypes were exclusive to Marchica Lagoon. The identified clades I, III, IV, VII, and 5.3 in Marchica were in accordance with its environmental characteristics. In addition, the relative abundance of some cooccurring Synechococcus strains was not constant over time and space (e.g., clades I and IV). Using gene oligotyping, we illustrated some of the challenges associated with the identification of novel Synechococcus strains or studied their co-occurrence in space and time. Oligotyping has been instrumental in discriminating closely related Synechococcus strains. However, this study leaves open questions about how samples differ by location and whether locations differ from year to year. Do cooccurring oligotypes interact with each other and to what extent do they correlate with physicochemical parameters? What triggers the coexistence of clades I and IV with clade III in warm water or 5.3 with VII, which do not know much about. Finally, how do relative abundances change over seasons. Hence, future work needs to consider additional stations and seasons to provide better statistical support for our findings and to better understand their correlation with physical and chemical environmental parameters. Other factors were not considered in this study, such as nutrient availability, chlorophyll, irradiance, viral lysis, and greater sequencing depth, which could also influence the observed seasonal dynamics. 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