Philippa Kaur

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S. & Zheng, X. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. (2015).Alberdi, A., Aizpurua, O., Gilbert, M. T. P. & Bohmann, K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol. Evol. 9, 134–147 (2018).Article 

Google Scholar 
Albert, J. S. & Reis, R. E. One. Introduction to Neotropical freshwaters. In Historical biogeography of Neotropical freshwater fishes (pp. 3-20). University of California Press. (2011).Allard, L., Popée, M., Vigouroux, R. & Brosse, S. Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages. Aquat. Sci. 78, 315–325 (2016).Article 

Google Scholar 
Berry, O. et al. Making environmental DNA (eDNA) biodiversity records globally accessible. Environ. DNA 3(4), 699–705 (2020).Article 

Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29(6), 358–367 (2014).PubMed 
Article 

Google Scholar 
Bolyen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Nat. Biotechnol. 32, 852–857 (2019).Article 
CAS 

Google Scholar 
Bonder, M. J., Abeln, S., Zaura, E. & Brandt, B. W. Comparing clustering and pre-processing in taxonomy analysis. Bioinformatics 28(22), 2891–2897 (2012).CAS 
PubMed 
Article 

Google Scholar 
Boussarie, G. et al. Environmental DNA illuminates the dark diversity of sharks. Sci. Adv. 4, eaap9661 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boyer, F. et al. obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecology Resour. 16(1), 176–182 (2016).CAS 
Article 

Google Scholar 
Brandt, M.I., Trouche, B., Quintric, L., Günther, B., Wincker, P., Poulain, J. & Arnaud-Haond, S. Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding. Molecular Ecology Resources. Accepted (2021).Brosse, S., Melki, F. & Vigouroux, R. Fishes from the Mitaraka mountains (French Guiana). Zoosystema 41, 131–151 (2019).Article 

Google Scholar 
Brown, E. A., Chain, F. J., Crease, T. J., MacIsaac, H. J. & Cristescu, M. E. Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?. Ecol. Evol. 5(11), 2234–2251 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Busia, K., George, D. E., Fannjiang, C., Alexander, D.H., Dorfman, E., Poplin, R., Chang, P., & DePris, M. A deep learning approach to pattern recognition for short DNA sequences. BioRxiv (2020).Bylemans, J., Gleeson, D. M., Hardy, C. M. & Furlan, E. Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia). Ecol. Evol. 8(17), 8697–8712 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Calderón-Sanou, I., Münkemüller, T., Boyer, F., Zinger, L. & Thuiller, W. From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices?. J. Biogeogr. 47(1), 193–206 (2020).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cantera, I., Coutant, O., Jézéuel, C., Decotte, J.B., Dejean, T., Vigouroux, R., Valentini, A. Murienne, J. & Brosse S. Slight deforestation causes harsh biodiversity decline in Amazonian rivers (submitted)Cantera, I., Decotte, J. B., Dejean, T., Murienne, J., Vigouroux, R., Valentini, A., & Brosse, S. Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach. BioRxiv (2020).Cantera, I. et al. Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Sci. Rep. 9(1), 1–1 (2019).CAS 
Article 

Google Scholar 
Cardoso, Y. P. & Montoya-Burgos, J. I. Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region. Mol. Ecol. 18, 947–964 (2009).CAS 
PubMed 
Article 

Google Scholar 
Cilleros, K. et al. Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes. Mol. Ecol. Resour. 19(1), 27–46 (2019).CAS 
PubMed 
Article 

Google Scholar 
Collen, B., Ram, M., Zamin, T. & McRae, L. The tropical biodiversity data gap: Addressing disparity in global monitoring. Trop. Conserv. Sci. 1(2), 75–88 (2008).Article 

Google Scholar 
Cordier, T., Lanzén, A., Apothéloz-Perret-Gentil, L., Stoeck, T. & Pawlowski, J. Embracing environmental genomics and machine learning for routine biomonitoring. Trends Microbiol. 27(5), 387–397 (2019).CAS 
PubMed 
Article 

Google Scholar 
Cordier, T. et al. Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap. Mol. Ecol. 30(13), 2937–2958 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Coutant, O. et al. Detecting fish assemblages with environmental DNA: Does protocol matter? Testing eDNA metabarcoding method robustness. Environ. DNA 3(3), 619–630 (2020).Article 

Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26(21), 5872–5895 (2017).PubMed 
Article 

Google Scholar 
Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F., & Joly, A. Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput. Biol. (in press) (2021).de Mérona, B., Tejerina-Garro, F. L. & Vigouroux, R. Fish-habitat relationships in French Guiana rivers: A review. Cybium 36, 7–15 (2012).
Google Scholar 
DiBattista, J. D. et al. Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems. Sci. Rep. 10(1), 1–15 (2020).Article 
CAS 

Google Scholar 
Dornelas, M., Madin, E. M., Bunce, M., DiBattista, J. D., Johnson, M., Madin, J. S., Magurran, A. E., McGill, B. J., Pettorelli, N., Pizarro, O. & Williams, S. B. Towards a macroscope: Leveraging technology to transform the breadth, scale and resolution of macroecological data. Glob. Ecol. Biogeogr. (2019).Dufresne, Y., Lejzerowicz, F., Perret-Gentil, L. A., Pawlowski, J. & Cordier, T. SLIM: A flexible web application for the reproducible processing of environmental DNA metabarcoding data. BMC Bioinform. 20(1), 1–6 (2019).Article 

Google Scholar 
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4(4), 423–425 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding?. Mol. Ecol. Resour. 16(3), 604–607 (2016).CAS 
PubMed 
Article 

Google Scholar 
Ficetola, G. F. et al. Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol. Ecology Resour. 15(3), 543–556 (2015).CAS 
Article 

Google Scholar 
Flynn, J. M., Brown, E. A., Chain, F. J., MacIsaac, H. J. & Cristescu, M. E. Toward accurate molecular identification of species in complex environmental samples: Testing the performance of sequence filtering and clustering methods. Ecol. Evol. 5(11), 2252–2266 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Gold, Z. et al. eDNA metabarcoding bioassessment of endangered fairy shrimp (Branchinecta spp.). Conserv. Genet. Resour. 12, 685–690 (2020).Article 

Google Scholar 
Grünig, M., Razavi, E., Calanca, P., Mazzi, D., Wegner, J. D., & Pellissier, L. Applying deep neural networks to predict incidence and phenology of plant pests and diseases. Ecosphere (accepted) (2021).Helaly, M. A., Rady, S., & Aref, M. M. Convolutional neural networks for biological sequence taxonomic classification: A comparative study. In International Conference on Advanced Intelligent Systems and Informatics (pp. 523–533). Springer, Cham (2019).Holman, L. E. et al. Animals, protists and bacteria share marine biogeographic patterns. Nat. Ecol. Evol. 5(6), 738–746 (2021).PubMed 
Article 

Google Scholar 
Iknayan, K. J., Tingley, M. W., Furnas, B. J. & Beissinger, S. R. Detecting diversity: Emerging methods to estimate species diversity. Trends Ecol. Evol. 29(2), 97–106 (2014).PubMed 
Article 

Google Scholar 
Jarman, S. N., Berry, O. & Bunce, M. The value of environmental DNA biobanking for long-term biomonitoring. Nat. Ecol. Evol. 2(8), 1192–1193 (2018).PubMed 
Article 

Google Scholar 
Juhel, J. B., Utama, R. S., Marques, V., Vimono, I. B., Sugeha, H. Y., Kadarusman, Pouyaud, L., Dejean, T., Mouillot, D. & Hocdé, R. Accumulation curves of environmental DNA sequences predict coastal fish diversity in the coral triangle. Proc. R. Soc. B 287(1930), 20200248 (2020).Kopp, W., Monti, R., Tamburrini, A., Ohler, U. & Akalin, A. Deep learning for genomics using Janggu. Nat. Commun. 11(1), 1–7 (2020).Article 
CAS 

Google Scholar 
Le Bail, P. Y. et al. Updated checklist of the freshwater and estuarine fishes of French Guiana. Cybium 36(1), 293–319 (2012).
Google Scholar 
LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989).Article 

Google Scholar 
Li, W. et al. Validating eDNA measurements of the richness and abundance of anurans at a large scale. J. Anim. Ecol. 90(6), 1466–1479 (2021).PubMed 
Article 

Google Scholar 
Lopes, C. M. et al. eDNA metabarcoding: A promising method for anuran surveys in highly diverse tropical forests. Mol. Ecol. Resour. 17(5), 904–914 (2017).CAS 
PubMed 
Article 

Google Scholar 
Makiola, A. et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 7, 197 (2020).Article 

Google Scholar 
Marques, V. et al. Blind assessment of vertebrate taxonomic diversity across spatial scales by clustering environmental DNA metabarcoding sequences. Ecography 43(12), 1779–1790 (2020).Article 

Google Scholar 
Marques, V. et al. GAPeDNA: Assessing and mapping global species gaps in genetic databases for eDNA metabarcoding. Divers. Distrib. 27(10), 1880–1892 (2020).Article 

Google Scholar 
Mathon, L. et al. Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification. Mol. Ecol. Resour. 21(7), 2565–2579 (2021).CAS 
PubMed 
Article 

Google Scholar 
McGee, K. M., Robinson, C. & Hajibabaei, M. Gaps in DNA-based biomonitoring across the globe. Front. Ecol. Evol. 7, 337 (2019).Article 

Google Scholar 
Murienne, J. et al. Aquatic eDNA for monitoring French Guiana biodiversity. Biodivers. Data J. 7, e37518 (2019).Nugent, C. M. & Adamowicz, S. J. Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding Metagenomics 4, e55815 (2020).Pagni, M. et al. Density-based hierarchical clustering of pyro-sequences on a large scale-the case of fungal ITS1. Bioinformatics 29(10), 1268–1274 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Papa, Y., Le Bail, P. Y. & Covain, R. Genetic landscape clustering of a large DNA barcoding dataset reveals shared patterns of genetic divergence among freshwater fishes of the Maroni Basin. Authorea Preprints (2020).Piro, V. C., Dadi, T. H., Seiler, E., Reinert, K. & Renard, B. Y. ganon: Precise metagenomics classification against large and up-to-date sets of reference sequences. Bioinformatics 36(Supplement 1), i12–i20 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Polanco Fernández, A., Marques, V., Fopp, F., Juhel, J. B., Borrero-Pérez, G. H., Cheutin, M. C., Eme, D. & Pellissier, L. Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environ. DNA 3, 142–156 (2021).Polanco, A. et al. Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems. Environ. DNA 3(6), 1113–1127 (2021).Article 

Google Scholar 
Polanco Fernández, A., Martinezguerra, M. M., Marques, V., Francisco Villa-Navarro, Borrero-Pérez, G. H., Cheutin, M. C., Dejean, T., Hocdé, R., Juhel, J. B., Maire, E., Manel, S. & Pellissier, L. Recovering aquatic and terrestrial biodiversity in a tropical estuary using environmental DNA. Biotropica 53(6), 1606–1619 (2021).Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, 1–22 (2016).Article 

Google Scholar 
Rojahn, J., Gleeson, D. M., Furlan, E., Haeusler, T. & Bylemans, J. Improving the detection of rare native fish species in environmental DNA metabarcoding surveys. Aquat. Conserv. Mar. Freshw. Ecosyst. 31(4), 990–997 (2021).Article 

Google Scholar 
Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).Article 

Google Scholar 
Sato, Y., Miya, M., Fukunaga, T., Sado, T. & Iwasaki, W. MitoFish and MiFish pipeline: A mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Mol. Biol. Evol. 35(6), 1553–1555 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schirmer, M. et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43(6), e37 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated–reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15(6), 1289–1303 (2015).CAS 
PubMed 
Article 

Google Scholar 
Sepulveda, A. J., Nelson, N. M., Jerde, C. L. & Luikart, G. Are environmental DNA methods ready for aquatic invasive species management?. Trends Ecol. Evol. 35, 668–678 (2020).PubMed 
Article 

Google Scholar 
Shokralla, S., Spall, J. L., Gibson, J. F. & Hajibabaei, M. Next-generation sequencing technologies for environmental DNA research. Mol. Ecol. 21(8), 1794–1805 (2012).CAS 
PubMed 
Article 

Google Scholar 
Shorten, C. & Khoshgoftaar, T. A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019).Article 

Google Scholar 
Singer, G. A. C., Fahner, N. A., Barnes, J. G., McCarthy, A. & Hajibabaei, M. Comprehensive biodiversity analysis via ultra-deep patterned flow cell technology: A case study of eDNA metabarcoding seawater. Sci. Rep. 9(1), 1–12 (2019).ADS 
CAS 
Article 

Google Scholar 
Su, G. et al. Human impacts on global freshwater fish biodiversity. Science 371(6531), 835 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Taberlet, P., Bonin, A., Coissac, E. & Zinger, L. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, Oxford, 2018).Book 

Google Scholar 
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21(8), 2045–2050 (2012).CAS 
PubMed 
Article 

Google Scholar 
Thomsen, P. F. & Willerslev, E. Environmental DNA-An emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD–A platform for ensemble forecasting of species distributions. Ecography 32(3), 369–373 (2009).Article 

Google Scholar 
Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25(4), 929–942 (2016).CAS 
PubMed 
Article 

Google Scholar 
West, K. et al. Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia. Divers. Distrib. 27(10), 1942–1957 (2021).Article 

Google Scholar  More

Lombardo et al. argue that, if our hypothesis is correct, ADEs should be continuous rather than patchy. However, alluvium deposition can be a patchy process and the distribution of large and small ADE patches can be predicted regionally based on fluvial geomorphology. For example, 89% of all known ADEs have been predictively mapped using elevation, distance to bluff, and geological provenance as the key predictors (with a false negative rate of 6.5% and a false positive rate of 4.7%)10. Predicted areas include small and large ADE patches, up to several square kilometres in size, and indicate that ADEs cover ~154,000 km2 mostly in central and western Amazonia. This may seem to be a very large area ( >3% of the Amazon basin) but it is only a fraction of the projections found in some of the most cited anthropogenic theory literature11. Assuming the same excess fertility observed at our site, the creation of those ADEs would have required a prohibitive amount of biomass burning, in areas 800–1680 times larger (Fig. 1), which is inconsistent with the centralised small-scale deposition proposed by Lombardo et al. In this regional scenario, it remains unclear how many Amazons would have been needed to build the already-mapped ADEs.Lombardo et al. centre their opinion on settlements in other parts of the Amazon basin, under different socioecological and geomorphological contexts, and where the data we have developed are not available for comparison. Their narrative conflates the Brazilian lowland with other regions, such as the Llanos de Moxos and other systems in the Bolivian-Peruvian foreland basins, where older archeological sites occur. Their comments about the mineral composition of ADEs appear to contradict recent discoveries (made by some of their co-authors)12 which show that some oxides found at our ADE site bear “no relationship to anthropogenic activity” because “their sources are attributed to the weathering of micas, feldspars, mafic minerals (pyroxene), and sodic plagioclase” that are not found locally. To explain the inconsistency between those findings and the current theory of ADE formation, Macedo et al. argue that “sediment depositions in floodplain soils” that “are not related to human occupation” should be considered. That suggestion is consistent with our data which indicate deposition of exogenous materials to the site prior to the invention of agriculture in central Amazonia.Our study area is on a Tertiary terrace, and we acknowledge in our paper that it lies above the modern 100-year flood height for Manaus. However, significant Pleistocene and Holocene tectonic activity and river aggradation/degradation demonstrably affected the flood height over time. A complex neotectonic history has affected terrace elevations, nutrient deposition, and remobilisation, as well as flood heights and aggradation, resulting in higher base levels that were many metres above flood waters today in past millennia13,14,15. In addition, rivers transported and dispersed sediments from the Andes to the lowland, which were re-mobilised, and re-deposited in patchy patterns, from floodplains several times between 20 and 5 thousand years ago16,17,18. Such mineral inputs by past avulsion events may have occurred earlier in the Quaternary and remain as a relict soil where it has not subsequently eroded19. The older weathered sediments on the upper terraces lining the river look nothing like recent alluvium and the distribution of elements and their assemblages at our site are consistent with alluvial deposits in other sites. This process is explained in studies cited by Lombardo et al. (e.g., Pupim et al.), which note several periods of river aggradation, that support our hypothesis.As explained in our original paper, our data do not preclude a more recent human effect on the local landscape. The wisdom of indigenous populations, manifested in the application of waste materials to agricultural sites (since at least the late Holocene), may have further enriched ADEs or countered their natural degradation. Recent studies12, 16, 17, which post-date the studies that Lombardo et al. cite to argue against a geogenic influence, reveal a dynamic neotectonic history and support our hypothesis. Thus, the extent to which other ADE sites originated from depositional processes should be investigated based on evidence that goes beyond those presented by Lombardo et al. More

Derraik, J. G. The pollution of the marine environment by plastic debris: A review. Mar. Pollut. Bull. 44(9), 842–852 (2002).CAS 
PubMed 

Google Scholar 
Gregory, M. R. Environmental implications of plastic debris in marine settings—Entanglement, ingestion, smothering, hangers-on, hitch-hiking and alien invasions. Philos. Trans. R. Soc. B Biol. Sci. 364(1526), 2013–2025 (2009).
Google Scholar 
Claessens, M., Van Cauwenberghe, L., Vandegehuchte, M. B. & Janssen, C. R. New techniques for the detection of microplastics in sediments and field collected organisms. Mar. Pollut. Bull. 70(1–2), 227–233 (2013).CAS 
PubMed 

Google Scholar 
Auta, H. S., Emenike, C. U. & Fauziah, S. H. Distribution and importance of microplastics in the marine environment: A review of the sources, fate, effects, and potential solutions. Environ. Int. 102, 165–176 (2017).CAS 
PubMed 

Google Scholar 
Zobkov, M. B. & Esiukova, E. E. Microplastics in a Marine Environment: Review of Methods for Sampling, Processing, and Analyzing Microplastics in Water, Bottom Sediments, and Coastal Deposits (2018).Coyle, R., Hardiman, G. & O’Driscoll, K. Microplastics in the marine environment: A review of their sources, distribution processes, uptake and exchange in ecosystems. Case Stud. Chem. Environ. Eng. 2, 100010 (2020).
Google Scholar 
Barnes, D. K., Galgani, F., Thompson, R. C. & Barlaz, M. Accumulation and fragmentation of plastic debris in global environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1985–1998 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
GESAMP. Sources, Fate and Effects of Microplastics in the Marine Environment: Part 2 of a Global Assessment. A Report to Inform the Second United Nations Environment Assembly, 220 (Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection, 2016).
Google Scholar 
Kroon, F. J., Motti, C. E., Jensen, L. H. & Berry, K. L. Classification of marine microdebris: A review and case study on fish from the Great Barrier Reef, Australia. Sci. Rep. 8(1), 1–15. https://doi.org/10.1038/s41598-018-34590-6 (2018).CAS 
Article 

Google Scholar 
Cole, M., Lindeque, P., Halsband, C. & Galloway, T. Microplastics as contaminants in the marine environment: A review. Mar. Pollut. Bull. 62(12), 2588–2597 (2011).CAS 
PubMed 

Google Scholar 
Cole, M. A novel method for preparing microplastic fibers. Sci. Rep. 6(1), 1–7. https://doi.org/10.1038/srep34519 (2016).CAS 
Article 

Google Scholar 
Costa, M. et al. On the importance of size of plastic fragments and pellets on the strandline: A snapshot of a Brazilian beach. Environ. Monit. Assess. 168, 299–304 (2010).PubMed 

Google Scholar 
Kershaw, P. J. et al. (eds) GESAMP Guidelines or the Monitoring and Assessment of Plastic Litter and Microplastics in the Ocean, Rep. Stud. GESAMP No. 99 130 (IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP/UNDP/ISA Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection, 2019).
Google Scholar 
Lusher, A. L., Welden, N. A., Sobral, P. & Cole, M. Sampling, isolating and identifying microplastics ingested by fish and invertebrates. Anal. Methods 9, 1346 (2017).
Google Scholar 
Lusher, A., Bråte, I. L. N., Hurley, R., Iversen, K. & Olsen, M. Testing of Methodology for Measuring Microplastics in Blue Mussels (Mytilus spp) and Sediments, and Recommendations for Future Monitoring of Microplastics (R & D-project) (2017).Laist, D. W. Impacts of marine debris: Entanglement of marine life in marine debris including a comprehensive list of species with entanglement and ingestion records. In Marine debris, 99–139 (Springer, 1997).Denuncio, P. et al. Plastic ingestion in Franciscana dolphins, Pontoporia blainvillei (Gervais and d’Orbigny, 1844), from Argentina. Mar. Pollut. Bull. 62(8), 1836–1841 (2011).CAS 
PubMed 

Google Scholar 
Do Sul, J. A. I., Santos, I. R., Friedrich, A. C., Matthiensen, A. & Fillmann, G. Plastic pollution at a sea turtle conservation area in NE Brazil: Contrasting developed and undeveloped beaches. Estuar. Coasts 34(4), 814–823 (2011).
Google Scholar 
Lazar, B. & Gračan, R. Ingestion of marine debris by loggerhead sea turtles, Caretta caretta, in the Adriatic Sea. Mar. Pollut. Bull. 62(1), 43–47 (2011).CAS 
PubMed 

Google Scholar 
Poppi, L. et al. Post-mortem investigations on a leatherback turtle Dermochelys coriacea stranded along the Northern Adriatic coastline. Dis. Aquat. Org. 100(1), 71–76 (2012).
Google Scholar 
Van Franeker, J. A. et al. Monitoring plastic ingestion by the northern fulmar Fulmarus glacialis in the North Sea. Environ. Pollut. 159(10), 2609–2615 (2011).PubMed 

Google Scholar 
Betts, K. Why Small Plastic Particles May Pose a Big Problem in the Oceans 8995–8995 (ACS Publications, 2008).
Google Scholar 
Cefas, L. Programme 8: Bass gillnet selectivity. Fish. Sci. 09 (2008).Priscilla, V., Sedayu, A. & Patria, M. P. Microplastic abundance in the water, seagrass, and sea hare Dolabella auricularia in Pramuka Island, Seribu Islands, Jakarta Bay, Indonesia. J. Phys. Conf. Ser. 1402, 033073. https://doi.org/10.1088/1742-6596/1402/3/033073 (2019).Article 

Google Scholar 
Graham, E. R. & Thompson, J. T. Deposit-and suspension-feeding sea cucumbers (Echinodermata) ingest plastic fragments. J. Exp. Mar. Biol. Ecol. 368(1), 22–29 (2009).
Google Scholar 
Thompson, R. C. et al. Lost at sea: Where is all the plastic? Science 304(5672), 838–838 (2004).CAS 
PubMed 

Google Scholar 
Hämer, J., Gutow, L., Köhler, A. & Saborowski, R. Fate of microplastics in the marine isopod Idotea emarginata. Environ. Sci. Technol. 48(22), 13451–13458 (2014).ADS 
PubMed 

Google Scholar 
Setälä, O., Fleming-Lehtinen, V. & Lehtiniemi, M. Ingestion and transfer of microplastics in the planktonic food web. Environ. Pollut. 185, 77–83 (2014).PubMed 

Google Scholar 
Cole, M. et al. Microplastics alter the properties and sinking rates of zooplankton faecal pellets. Environ. Sci. Technol. 50(6), 3239–3246 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Gutow, L., Eckerlebe, A., Giménez, L. & Saborowski, R. Experimental evaluation of seaweeds as a vector for microplastics into marine food webs. Environ. Sci. Technol. 50(2), 915–923 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Naji, A., Nuri, M. & Vethaak, A. D. Microplastics contamination in molluscs from the northern part of the Persian Gulf. Environ. Pollut. 235, 113–120 (2018).CAS 
PubMed 

Google Scholar 
Ding, J. et al. Detection of microplastics in local marine organisms using a multi-technology system. Anal. Methods 11(1), 78–87 (2019).CAS 

Google Scholar 
Gniadek, M. & Dąbrowska, A. The marine nano-and microplastics characterisation by SEM-EDX: The potential of the method in comparison with various physical and chemical approaches. Mar. Pollut. Bull. 148, 210–216 (2019).CAS 
PubMed 

Google Scholar 
Dąbrowska, A. A roadmap for a plastisphere. Mar. Pollut. Bull. 167, 112322 (2021).PubMed 

Google Scholar 
Ebere, E. C. & Ngozi, V. E. Microplastics, an emerging concern: A review of analytical techniques for detecting and quantifying microplatics. Anal. Methods Environ. Chem. J. 2(2), 13–30 (2019).
Google Scholar 
Mariano, S., Tacconi, S., Fidaleo, M., Rossi, M. & Dini, L. Micro and nanoplastics identification: Classic methods and innovative detection techniques. Front. Toxicol. https://doi.org/10.3389/ftox.2021.636640 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ferrante, M. et al. Microplastics in fillets of Mediterranean seafood. A risk assessment study. Environ. Res. 204, 112247 (2022).CAS 
PubMed 

Google Scholar 
Li, J. et al. Characterization, source, and retention of microplastic in sandy beaches and mangrove wetlands of the Qinzhou Bay, China. Mar. Pollut. Bull. 136, 401–406 (2018).CAS 
PubMed 

Google Scholar 
Liu, J. et al. Pollution characteristics of microplastics in mollusks from the coastal Area of Yantai. China. Bull. Environ. Contamin. Toxicol. 107, 1–7 (2021).
Google Scholar 
Tarjuelo, I., Posada, D., Crandall, K., Pascual, M. & Turon, X. Cryptic species of Clavelina (Ascidiacea) in two different habitats: Harbours and rocky littoral zones in the northwestern Mediterranean. Mar. Biol. 139(3), 455–462 (2001).
Google Scholar 
Brunetti, R. & Mastrototaro, F. Botrylloides pizoni, a new species of Botryllinae (Ascidiacea) from the Mediterranean Sea R. Zootaxa 3258(1), 28–36 (2012).
Google Scholar 
Beli, E. et al. The zoogeography of extant rhabdopleurid hemichordates (Pterobranchia: Graptolithina), with a new species from the Mediterranean Sea. Invertebr. Syst. 32(1), 100–110 (2018).
Google Scholar 
Chimienti, G., Angeletti, L., Furfaro, G., Canese, S. & Taviani, M. Habitat, morphology and trophism of Tritonia callogorgiae sp. nov., a large nudibranch inhabiting Callogorgia verticillata forests in the Mediterranean Sea. Deep Sea Res. I Oceanogr. Res. Pap. 165, 103364 (2020).
Google Scholar 
Furfaro, G. & Mariottini, P. A new Dondice Marcus Er. 1958 (Gastropoda: Nudibranchia) from the Mediterranean Sea reveals interesting insights into the phylogenetic history of a group of Facelinidae taxa. Zootaxa 4731(1), 1–22. https://doi.org/10.11646/zootaxa.4731.1.1 (2020).Article 

Google Scholar 
Cózar, A. et al. Plastic accumulation in the Mediterranean Sea. PLoS ONE 10(4), e0121762. https://doi.org/10.1371/journal.pone.0121762 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sharma, S., Sharma, V. & Chatterjee, S. Microplastics in the Mediterranean Sea: Sources, pollution intensity, sea health, and regulatory policies. Front. Mar. Sci. 8, 634934. https://doi.org/10.3389/fmars.2021.634934 (2021).Article 

Google Scholar 
Pinardi, N. & Masetti, E. Variability of the large scale general circulation of the Mediterranean Sea from observations and modelling: A review. Palaeogeogr. Palaeoclimatol. Palaeoecol. 158(3–4), 153–173 (2000).
Google Scholar 
Suaria, G. et al. The Mediterranean Plastic soup: Synthetic polymers in Mediterranean surface waters. Sci. Rep. 6(1), 1–10 (2016).
Google Scholar 
Vianello, A. et al. Microplastic particles in sediments of Lagoon of Venice, Italy: First observations on occurrence, spatial patterns and identification. Estuar. Coast. Shelf. Sci. 130, 54–61. https://doi.org/10.1016/j.ecss.2013.03.022 (2013).ADS 
CAS 
Article 

Google Scholar 
Parenzan, P. Il Mar Piccolo di Taranto. Ciem. Comm. Taranto (1984).Cavallo, R. A. & Stabili, L. Presence of vibrios in seawater and Mytilus galloprovincialis (Lam.) from the Mar Piccolo of Taranto (Ionian Sea). Water Res. 36(15), 3719–3726 (2002).CAS 
PubMed 

Google Scholar 
Cardellicchio, N. et al. Organic pollutants (PAHs, PCBs) in sediments from the Mar Piccolo in Taranto (Ionian Sea, Southern Italy). Mar. Pollut. Bull. 55(10–12), 451–458 (2007).CAS 
PubMed 

Google Scholar 
Cardellicchio, N., Annicchiarico, C., Di Leo, A., Giandomenico, S. & Spada, L. The Mar Piccolo of Taranto: An interesting marine ecosystem for the environmental problems studies. Environ. Sci. Pollut. Res. 23(13), 12495–12501 (2016).
Google Scholar 
Tursi, A. et al. Mega-litter and remediation: The case of Mar Piccolo of Taranto (Ionian Sea). Rendiconti Lincei. Sci. Fisiche e Nat. 29(4), 817–824 (2018).
Google Scholar 
Mastrototaro, F. et al. Benthic diversity of the soft bottoms in a semi-enclosed basin of the Mediterranean Sea. Marine Biological Association of the United Kingdom. J. Mar. Biol. Assoc. U.K. 88(2), 247 (2008).
Google Scholar 
Li, J. et al. Using mussel as a global bioindicator of coastal microplastic pollution. Environ. Pollut. 244, 522–533 (2019).CAS 
PubMed 

Google Scholar 
Corami, F. et al. Evidence of small microplastics (< 100 μm) ingestion by Pacific oysters (Crassostrea gigas): A novel method of extraction, purification, and analysis using Micro-FTIR. Mar. Pollut. Bull. 160, 111606 (2020).CAS  PubMed  Google Scholar  De-la-Torre, G. E., Apaza-Vargas, D. M. & Santillán, L. L. Microplastic ingestion and feeding ecology in three intertidal mollusk species from Lima, Peru. Rev. Biol. Mar. Oceanogr. 55(2), 167–171 (2020). Google Scholar  Jiang, Y. et al. A review of microplastic pollution in seawater, sediments and organisms of the Chinese coastal and marginal seas. Chemosphere 286, 131677 (2021).ADS  PubMed  Google Scholar  Haszprunar, G. The heterobranchia—A new concept of the phylogeny of the higher Gastropoda. J. Zool. Syst. Evol. Res. 23(1), 15–37 (1985). Google Scholar  Wägele, H., Klussmann-Kolb, A., Vonnemann, V. & Medina, M. Heterobranchia I: The Opisthobranchia. In Phylogeny and Evolution of the Mollusca (eds Ponder, W. F. & Lindberg, D.) 385–408 (University of California Press, 2008). Google Scholar  Prkic, J. et al. First record of Calma gobioophaga Calado and Urgorri, 2002 (Gastropoda: Nudibranchia) in the Mediterranean Sea. Mediterr. Mar. Sci. 15(2), 423–428 (2014). Google Scholar  Furfaro, G., Trainito, E., De Lorenzi, F., Fantin, M. & Doneddu, M. Tritonia nilsodhneri Marcus Ev., 1983 (Gastropoda, Heterobranchia, Tritoniidae): First records for the Adriatic Sea and new data on ecology and distribution of Mediterranean populations. Acta Adriat. 58, 2 (2017). Google Scholar  Thompson, T. E. Studies on ontogeny of Tritonia hombergi Cuvier (Gastropoda: Opisthobranchia). Philos. Trans. R. Soc. Lond. B 245, 171–218. https://doi.org/10.1098/rstb.1962.0009 (1962).ADS  Article  Google Scholar  Cattaneo-Vietti, R., Angelini, S. & Bavestrello, G. Skin and gut spicules in Discodoris atromaculata (Bergh, 1880) (Mollusca: Nudibranchia). Bollettino Malacol. 28, 173–180 (1993). Google Scholar  Cattaneo-Vietti, R., Angelini, S., Gaggero, L. & Lucchetti, G. Mineral composition of nudibranch spicules. J. Molluscan Stud. 61(3), 331–337. https://doi.org/10.1093/mollus/61.3.331 (1995).Article  Google Scholar  Garese, A., García-Matucheski, S., Acuña, F. H. & Muniain, C. Feeding behavior of Spurilla sp. (Mollusca: Opisthobranchia) with a description of the kleptocnidae sequestered from its sea anemone prey. Zool. Stud. 51(7), 905–912 (2012).CAS  Google Scholar  Braga, T. et al. Bursatella leachii from Mar Menor as a source of bioactive molecules: Preliminary evaluation of the nutritional profile, in vitro biological activities and fatty acids contents. J. Aquat. Food Prod. Technol. 26(10), 1337–1350 (2017).CAS  Google Scholar  Willis, T. J. et al. Kleptopredation: A mechanism to facilitate planktivory in a benthic mollusc. Biol. Let. 13, 20170447. https://doi.org/10.1098/rsbl.2017.0447 (2017).Article  Google Scholar  Goodheart, J. A. et al. Comparative morphology and evolution of the cnidosac in Cladobranchia (Gastropoda: Heterobranchia: Nudibranchia). Front. Zool. 15(1), 1–18. https://doi.org/10.1186/s12983-018-0289-2 (2018).CAS  Article  Google Scholar  Marin, A. & Ros, J. Chemical defenses in Sacoglossan Opisthobranchs: Taxonomic trends and evolutive implications. Sci. Mar. 67(Suppl. 1), 227–241 (2004). Google Scholar  Wägele, H., Ballestero, M. & Avila, C. Defensive glandular structures in opisthobranch molluscs—From histology to ecology. Oceanogr. Mar. Biol. Annu. Rev. 44, 197–276 (2006). Google Scholar  Pavlik, J. R. Antipredatory defensive roles of natural products from marine invertebrates. In Handbook of Marine Natural Products Vol. 12 (eds Fattorusso, E. et al.) 677–710 (Springer, 2012). Google Scholar  Avila, C., Nuñez-Pons, L. & Moles, J. From the tropics to the poles chemical defense strategies in sea slugs (Mollusca: Heterobranchia). In Chemical Ecology: The Ecological Impact of Marine Natural Products (eds Puglisi, M. P. & Becerro, M. A.) 93 (CRC Press, 2018). Google Scholar  Capper, A., Tibbetts, I. R., O’Neil, J. M. & Shaw, G. R. The fate of Lyngbya majuscula toxins in three potential consumers. J. Chem. Ecol. 31(7), 1595–1606 (2005).CAS  PubMed  Google Scholar  Dean, L. J. & Prinsep, M. R. The chemistry and chemical ecology of nudibranchs. Nat. Prod. Rep. 34(12), 1359–1390 (2017).CAS  PubMed  Google Scholar  Simmons, T. L., Andrianasolo, E., McPhail, K., Flatt, P. & Gerwick, W. H. Marine natural products as anticancer drugs. Mol. Cancer Ther. 4(2), 333–342 (2005).CAS  PubMed  Google Scholar  Klussmann-Kolb, A. Phylogeny of the Aplysiidae (Gastropoda, Opisthobranchia) with new aspects of the evolution of seahares. Zool. Scr. 33, 439–462 (2004). Google Scholar  Willan, R. C. Phylogenetic systematics of the Notaspidea (Opisthobranchia) with reappraisal of families and genera. Am. Malacol. Bull. 5, 215–241 (1987). Google Scholar  Medina, M. & Walsh, P. J. Molecular systematics of the order Anaspidea based on mitochondrial DNA sequences (12S, 16S, and COI). Mol. Phylogenet. Evol. 15, 41–58 (2000).CAS  PubMed  Google Scholar  Furfaro, G., De Matteo, S., Mariottini, P. & Giacobbe, S. Ecological notes of the alien species Godiva quadricolor (Gastropoda: Nudibranchia) occurring in Faro Lake (Italy). J. Nat. Hist. 52(11–12), 645–657 (2018). Google Scholar  Appleton, D. R., Sewell, M. A., Berridge, M. V. & Copp, B. R. A new biologically active malyngamide from a New Zealand collection of the sea hare Bursatella leachii. J. Nat. Prod. 65(4), 630–631 (2002).CAS  PubMed  Google Scholar  Rajaganapathi, J., Kathiresan, K. & Singh, T. P. Purification of anti-HIV protein from purple fluid of the sea hare Bursatella leachii de Blainville. Mar. Biotechnol. 4(5), 447–453 (2002).CAS  Google Scholar  Suntornchashwej, S., Chaichit, N., Isobe, M. & Suwanborirux, K. Hectochlorin and morpholine derivatives from the Thai Sea Hare, Bursatella leachii. J. Nat. Prod. 68(6), 951–955 (2005).CAS  PubMed  Google Scholar  Dhahri, M. et al. Extraction, characterization, and anticoagulant activity of a sulfated polysaccharide from Bursatella leachii viscera. ACS Omega 5(24), 14786–14795 (2020).CAS  PubMed  PubMed Central  Google Scholar  Clarke, C. L. The population dynamics and feeding preferences of Bursatella leachii (Opisthobranchia: Anaspidea) in northeast Queensland, Australia. Rec. West. Austral. Museum Suppl. 69, 11–21 (2006). Google Scholar  Blainville, H. M. D. de. Bursatella, p. 138, in: Dictionnaire des Sciences Naturelles (F. Cuvier, ed.), Vol. 5, Supplément. Levrault, Strasbourg & Le Normant, Paris (1817).Trainito, E. & Doneddu, M. Nudibranchi del Mediterraneo 2nd edn, 192 (Il Castello, 2014). Google Scholar  Zbyszewski, M., Corcoran, P. L. & Hockin, A. Comparison of the distribution and degradation of plastic debris along shorelines of the Great Lakes, North America. J. Great Lakes Res. 40(2), 288–299 (2014).CAS  Google Scholar  Wang, Z. M., Wagner, J., Ghosal, S., Bedi, G. & Wall, S. SEM/EDS and optical microscopy analyses of microplastics in ocean trawl and fish guts. Sci. Total Environ. 603, 616–626 (2017).ADS  PubMed  Google Scholar  Gewert, B., Plassmann, M. & MacLeod, M. Pathways for degradation of plastic polymers floating in the marine environment. Environ. Sci. Process. Impacts 17, 1513–1521 (2015).CAS  PubMed  Google Scholar  Gewert, B., Plassmann, M., Sandblom, O. & MacLeod, M. Identification of chain scission products released to water by plastic exposed to ultraviolet light. Environ. Sci. Technol. Lett. 5, 272–276 (2018).CAS  Google Scholar  Lang, M. et al. Fenton aging significantly affects the heavy metal adsorption capacity of polystyrene microplastics. Sci. Total Environ. 722, 137762 (2020).ADS  CAS  PubMed  Google Scholar  Ding, L., Mao, R., Ma, S., Guo, X. & Zhu, L. High temperature depended on the ageing mechanism of microplastics under different environmental conditions and its effect on the distribution of organic pollutants. Water Res. 174, 115634 (2020).CAS  PubMed  Google Scholar  Wang, F. et al. The influence of polyethylene microplastics on pesticide residue and degradation in the aquatic environment. J. Hazard. Mater. 394, 122517 (2020).CAS  PubMed  Google Scholar  Ouyang, Z. et al. The aging behavior of polyvinyl chloride microplastics promoted by UV-activated persulfate process. J. Hazard. Mater. 424, 127461 (2022).CAS  PubMed  Google Scholar  Dehaut, A. et al. Microplastics in seafood: Benchmark protocol for their extraction and characterization. Environ. Pollut. 215, 223–233 (2016).CAS  PubMed  Google Scholar  Besley, A., Vijver, M. G., Behrens, P. & Bosker, T. A standardized method for sampling and extraction methods for quantifying microplastics in beach sand. Mar. Pollut. Bull. 114(1), 77–83 (2017).CAS  PubMed  Google Scholar  Karami, A. et al. A high-performance protocol for extraction of microplastics in fish. Sci. Total Environ. 578, 485–494 (2017).ADS  CAS  PubMed  Google Scholar  Caron, A. G. et al. Ingestion of microplastic debris by green sea turtles (Chelonia mydas) in the Great Barrier Reef: Validation of a sequential extraction protocol. Mar. Pollut. Bull. 127, 743–751 (2018).CAS  PubMed  Google Scholar  Piarulli, S. et al. Microplastic in wild populations of the omnivorous crab Carcinus aestuarii: A review and a regional-scale test of extraction methods, including microfibres. Environ. Pollut. 251, 117–127 (2019).CAS  PubMed  Google Scholar  Pfohl, P. et al. Microplastic extraction protocols can impact the polymer structure. Microplast. Nanoplast. 1(1), 1–13 (2021). Google Scholar  Qiu, Q. et al. Extraction, enumeration and identification methods for monitoring microplastics in the environment. Estuar. Coast. Shelf Sci. 176, 102–109 (2016).ADS  CAS  Google Scholar  Lusher, A. L., Munno, K., Hermabessiere, L. & Carr, S. Isolation and extraction of microplastics from environmental samples: An evaluation of practical approaches and recommendations for further harmonization. Appl. Spectrosc. 74(9), 1049–1065 (2020).ADS  CAS  PubMed  Google Scholar  Bellasi, A., Binda, G., Pozzi, A., Boldrocchi, G. & Bettinetti, R. The extraction of microplastics from sediments: An overview of existing methods and the proposal of a new and green alternative. Chemosphere 278, 130357 (2021).ADS  CAS  PubMed  Google Scholar  Essa, A. M. & Khallaf, M. K. Antimicrobial potential of consolidation polymers loaded with biological copper nanoparticles. BMC Microbiol. 16(1), 1–8 (2016). Google Scholar  Etcheverry, M., Ferreira, M. L., Capiati, N. J., Pegoretti, A. & Barbosa, S. E. Strengthening of polypropylene–glass fiber interface by direct metallocenic polymerization of propylene onto the fibers. Compos. A Appl. Sci. Manuf. 39(12), 1915–1923 (2008). Google Scholar  Ivanič, A., Kravanja, G., Kidess, W., Rudolf, R. & Lubej, S. The influences of moisture on the mechanical, morphological and thermogravimetric properties of mineral wool made from basalt glass fibers. Materials 13(10), 2392 (2020).ADS  PubMed Central  Google Scholar  Kavad, B. V., Pandey, A. B., Tadavi, M. V. & Jakharia, H. C. A review paper on effects of drilling on glass fiber reinforced plastic. Procedia Technol. 14, 457–464 (2014). Google Scholar  Alsayed, S. H., Al-Salloum, Y. A. & Almusallam, T. H. Performance of glass fiber reinforced plastic bars as a reinforcing material for concrete structures. Compos. B Eng. 31(6–7), 555–567 (2000). Google Scholar  Fries, E. et al. Identification of polymer types and additives in marine microplastic particles using pyrolysis-GC/MS and scanning electron microscopy. Environ. Sci. Process Impacts 15(10), 1949–1956 (2013).CAS  PubMed  Google Scholar  Turner, A. & Filella, M. The influence of additives on the fate of plastics in the marine environment, exemplified with barium sulphate. Mar. Pollut. Bull. 158, 111352 (2020).CAS  PubMed  Google Scholar  Barathi, M., Kumar, A. S. K. & Rajesh, N. Efficacy of novel Al–Zr impregnated cellulose adsorbent prepared using microwave irradiation for the facile defluoridation of water. J. Environ. Chem. Eng. 1(4), 1325–1335 (2013).CAS  Google Scholar  Bahsis, L. et al. Cellulose-copper as bio-supported recyclable catalyst for the clickable azide-alkyne [3+2] cycloaddition reaction in water. Int. J. Biol. Macromol. 119, 849–856 (2018).CAS  PubMed  Google Scholar  Ibrahim, N. A., Eid, B. M., Abd El-Aziz, E., Abou Elmaaty, T. M. & Ramadan, S. M. Multifunctional cellulose-containing fabrics using modified finishing formulations. RSC Adv. 7(53), 33219–33230 (2017).ADS  CAS  Google Scholar  Van, H. T., Le Sy, H., Nguyen, T. M. L. & Nguyen, D. K. Application of Mussell-derived biosorbent to remove NH 4+ from aqueous solution: Equilibrium and Kinetics. SN Appl. Sci. 3(4), 1–12 (2021). Google Scholar  Lakshmanna, B. et al. Data on Molluscan Shells in parts of Nellore Coast, southeast coast of India. Data Brief 16, 705–712 (2018).CAS  PubMed  Google Scholar  Taylor, P. D., Vinn, O., Kudryavtsev, A. & Schopf, J. W. Raman spectroscopic study of the mineral composition of cirratulid tubes (Annelida, Polychaeta). J. Struct. Biol. 171(3), 402–405 (2010).CAS  PubMed  Google Scholar  Schröder, V. et al. Micromorphological details and identification of chitinous wall structures in Rapana venosa (Gastropoda, Mollusca) egg capsules. Sci. Rep. 10(1), 1–13 (2020). Google Scholar  Ngamniyom, A., Wongroj, W., Karnchaisri, K. & Siriwattanarat, R. Ophidascaris baylisi (Nematoda: Ascarididae): Scanning electron microscopic study of the adult surface with ultrastructure and chemical composition analysis of eggshells. Sci. Technol. Asia 26, 189–198 (2021). Google Scholar  Fabra, M. et al. The plastic Trojan horse: Biofilms increase microplastic uptake in marine filter feeders impacting microbial transfer and organism health. Sci. Total Environ. 797, 149217 (2021).ADS  CAS  PubMed  Google Scholar  Jacquin, J. et al. Microbial ecotoxicology of marine plastic debris: A review on colonization and biodegradation by the “Plastisphere”. Front. Microbiol. 10, 865 (2019).PubMed  PubMed Central  Google Scholar  More

Engel, M. S. A new interpretation of the oldest fossil bee (Hymenoptera: Apidae). Am. Mus. Novit. 3296, 1–11 (2000).Article 

Google Scholar 
Michener, C. D. The Bees of the World 2nd edn, (John Hopkins University Press, 2007).Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B. 274, 303–313 (2007).PubMed 
Article 

Google Scholar 
Fürst, M., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
McMahon, D. P., Wilfert, L., Paxton, R. J. & Brown, M. J. F. Emerging viruses in bees: from molecules to ecology. Adv. Virus Res. 101, 251–291 (2015).Article 

Google Scholar 
Koch, H., Abrol, D. P., Li, J. & Schmid-Hempel, P. Diversity of evolutionary patterns of bacterial gut associates of corbiculate bees. Mol. Ecol. 22, 2028–2044 (2013).CAS 
PubMed 
Article 

Google Scholar 
McFrederick, Q. S. et al. Environment or kin: whence do bees obtain acidophilic bacteria? Mol. Ecol. 21, 1754–1768 (2012).PubMed 
Article 

Google Scholar 
McFrederick, Q. S., Wcislo, W. T., Hout, M. C. & Mueller, U. G. Host species and developmental stage, but not host social structure, affects bacterial community structure in social polymorphic bees. FEMS Microbiol. Ecol. 88, 398–406 (2014).CAS 
PubMed 
Article 

Google Scholar 
McFrederick, Q. S. et al. Flowers and wild megachilid bees share microbes. Microb. Ecol. 73, 188–200 (2017).PubMed 
Article 

Google Scholar 
Jones, J. C. et al. The gut microbiome is associated with behavioural task in honey bees. Insectes Sociaux 65, 419–429 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kristensen, T. N., Schonherz, A., Rohde, P. D., Sorensen, J. G. & Loeschcke, V. Strong experimental support for the hologenome hypothesis revealed from Drosophila melanogaster selection lines. bioRxiv https://doi.org/10.1101/2021.09.09.459587 (2021)Bovo, S., Utzeri, V. J., Ribani, A., Cabbri, R. & Fontanesi, L. Shotgun sequencing of honey DNA can describe honey bee derived environmental signatures and the honey bee hologenome complexity. Sci. Rep. 10, 1–17 (2020).Article 
CAS 

Google Scholar 
Dharampal, P. S., Carlson, C., Currie, C. R. & Steffan, S. A. Pollen-borne microbes shape bee fitness. Proc. R. Soc. B. 286, 20182894 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Graystock, P., Rehan, S. M. & McFrederick, Q. S. Hunting for healthy microbiomes: determining the core microbiomes of Ceratina, Megalopta, and Apis bees and how they associate with microbes in bee collected pollen. Conserv. Genet. 18, 701–711 (2017).Article 

Google Scholar 
Engel, P. et al. The bee microbiome: impact on bee health and model for evolution and ecology of host-microbe interactions. MBio 7, e02164–15 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Voulgari-Kokota, A., McFrederick, Q. S., Steffan-Dewenter, I. & Keller, A. Drivers, diversity, and functions of the solitary-bee microbiota. Trends Microbiol 27, 1034–1044 (2019).CAS 
PubMed 
Article 

Google Scholar 
Rothman, J. A., Leger, L., Graystock, P., Russell, K. & McFrederick, Q. S. The bumble bee microbiome increases survival of bees exposed to selenate toxicity. Environ. Microbiol. 21, 3417–3429 (2019).CAS 
Article 

Google Scholar 
Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honey bee. PNAS 109, 11002–11007 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Engel, P. & Moran, N. A. Functional and evolutionary insights into the simple yet specific gut microbiota of the honey bee from metagenomic analysis. Gut Microbes 4, 60–65 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Kwong, W. K. et al. Dynamic microbiome evolution in social bees. Sci. Adv. 3, e1600513 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Breeze, T. D., Bailey, A. P., Balcombe, K. G. & Potts, S. G. Pollination services in the UK: How important are honeybees? Agric. Ecosyst. Environ. 142, 137–143 (2011).Article 

Google Scholar 
Dharampal, P. S., Hetherington, M. C. & Steffan, S. A. Microbes make the meal: oligolectic bees require microbes within their host pollen to thrive. Ecol. Entomol. 45, 1418–1427 (2020).Article 

Google Scholar 
Keller, A. et al. (More than) hitchhikers through the network: the shared microbiome of bees and flowers. Curr. Opin. Insect 44, 8–15 (2021).Article 

Google Scholar 
Hugenholtz, P. & Tyson, G. W. Metagenomics. Nature 455, 481–483 (2008).CAS 
PubMed 
Article 

Google Scholar 
Galbraith, D. A. et al. Investigating the viral ecology of global bee communities with high- throughput metagenomics. Sci. Rep. 8, 8879 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Regan, T. et al. Characterisation of the British honey bee metagenome. Nat. Commun. 9, 1–13 (2018).CAS 
Article 

Google Scholar 
Bovo, S. et al. Shotgun metagenomics of honey DNA: Evaluation of a methodological approach to describe a multi-kingdom honey bee derived environmental DNA signature. PLOS ONE 13, e0205575 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schoonvaere, K. et al. Unbiased RNA shotgun metagenomics in social and solitary wild bees detects associations with eukaryote parasites and new viruses. PLOS ONE 11, e0168456 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cox-Foster, D. L. et al. A metagenomic survey of microbes in honey bee colony collapse disorder. Science 318, 283–287 (2007).CAS 
PubMed 
Article 

Google Scholar 
Rehan, S. M., Leys, R. & Schwarz, M. P. A mid-cretaceous origin of sociality in xylocopine bees with only two origins of true worker castes. PLOS ONE 7, e34690 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rehan, S. M. Small carpenter bees (Ceratina). Encyclopedia of Social Insects (ed Chris, S.) (Springer, 2020).Sakagami, S. F. & Maeta, Y. Multifemale nests and rudimentary castes in the normally solitary bee Ceratina japonica (Hymenoptera: Xylocopinae). J. Kans. Entomol. 57, 639–656 (1984).
Google Scholar 
Huisken, J. L., Shell, W. A., Pare, H. K. & Rehan, S. M. The influence of social environment on cooperating and conflict in an incipiently social bee, Ceratina calcarata. Behav. Ecol. 75, 74 (2021).Article 

Google Scholar 
Rehan, S. M., Glastad, K. M., Lawson, S. P. & Hunt, B. G. The genome and methylome of a subsocial small carpenter bee, Ceratina calcarata. GBE 8, 1401–1410 (2016).PubMed 
PubMed Central 

Google Scholar 
Rehan, S. M. et al. Conserved genes underlie phenotypic plasticity in an incipiently social bee. GBE 10, 2749–2758 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arsenault, S. V., Hunt, B. G. & Rehan, S. M. The effect of maternal care on gene expression and DNA methylation in a subsocial bee. Nat. Commun. 9, 3468 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Shell, W. A. et al. Sociality sculpts similar patterns of molecular evolution in two independently evolved lineages of eusocial bees. Comms. Biol. 4, 1–9 (2021).Article 
CAS 

Google Scholar 
Dew, R. M., McFrederick, Q. S. & Rehan, S. M. Diverse diets with consistent core microbiome in wild bee pollen provisions. Insects 11, 49 (2020).Article 

Google Scholar 
Lawson, S. P., Kennedy, K. & Rehan, S. M. Pollen composition significantly impacts development and survival of the native small carpenter bee, Ceratina calcarata. Ecol. Entomol. 46, 232–239 (2021).Article 

Google Scholar 
Oppenheimer, R. L., Shell, W. A. & Rehan, S. M. Phylogeography and population genetics of the Australian small carpenter bee, Ceratina australensis. Biol. J. Linn. Soc. 124, 747–755 (2018).Article 

Google Scholar 
McFrederick, Q. S. & Rehan, S. M. Wild bee pollen usage and microbial communities co- vary across landscapes. Microb. Ecol. 77, 513–522 (2018).PubMed 
Article 
CAS 

Google Scholar 
Rehan, S. M., Richards, M. H. & Schwarz, M. P. Sociality in the Australian small carpenter bee Ceratina (Neoceratina) australensis. Insectes Sociaux 57, 403–412 (2010).Article 

Google Scholar 
Harpur, B. A. & Rehan, S. M. Connecting social polymorphism to single nucleotide polymorphism: population genomics of the small carpenter bee, Ceratina australensis. Biol. J. Linn. Soc. 132, 945–954 (2021).Article 

Google Scholar 
Neu, A. T., Allen, E. E. & Roy, K. Defining and quantifying the core microbiome: challenges and prospects. PNAS 118, e2104429118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lawson, S. P., Ciaccio, K. N. & Rehan, S. M. Maternal manipulation of pollen provisions affects worker production in a small carpenter bee. Behav. Ecol. 70, 1891–1900 (2016).Article 

Google Scholar 
Ganeshprasad, D. N., Jani, K., Shouche, Y. S. & Sneharani, A. H. Gut bacterial inhabitants of open nested honey bee, Apis florea. Preprint at https://assets.researchsquare.com/files/rs-225332/v1/ddf21abe-2456-4f45-af61-4ba3e81d16e7.pdf?c=1641312753 (2021).Rothman, J. A., Cox-Foster, D. L., Andrikopoulos, C. & McFrederick, Q. S. Diet breadth affects bacterial identity but not diversity in the pollen provisions of closely related polylectic and oligolectic bees. Insects 11, 1–13 (2020).Article 

Google Scholar 
Cohen, H., McFrederick, Q. S. & Philpott, S. M. Environment shapes the microbiome of the blue orchard bee, Osmia lignaria. Microb. Ecol. 80, 897–907 (2020).PubMed 
Article 

Google Scholar 
Dew, R. M., Rehan, S. M. & Schwarz, M. P. Biogeography and demography of an Australian native bee Ceratina australensis (Hymenoptera: Apidae) since the last glacial maximum. J. Hymenopt. Res. 49, 25–41 (2016).Article 

Google Scholar 
Pinto-Tomás, A. A. et al. Symbiotic nitrogen fixation in the fungus gardens of leaf-cutter ants. Science 326, 1120–1123 (2009).PubMed 
Article 
CAS 

Google Scholar 
Walterson, A. M. & Stavrinides, J. Pantoea insights into a highly versatile and diverse genus within the Enterobacteriaceae. FEMS Microbiol. Rev. 39, 968–984 (2015).CAS 
PubMed 
Article 

Google Scholar 
Scheiner, R., Strauß, S., Thamm, M., Farré-Armengol, G. & Junker, R. R. The bacterium Pantoea ananatis modifies behavioral responses to sugar solutions in honeybees. Insects 11, 692 (2020).PubMed Central 
Article 

Google Scholar 
Leonhardt, S. D. & Kaltenpoth, M. Microbial communities of three sympatric Australian stingless bee species. Plos ONE 9, e105718 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bailey, L. & Ball, B. V. Honey Bee Pathology (Academic Press, 1991).Tham, V. L. Isolation of Streptococcus pluton from the larvae of European honey bees in Australia. Aust. Vet. J. 54, 406–407 (1978).CAS 
PubMed 
Article 

Google Scholar 
Bowman, J. The genus Flavobacterium. Prokaryotes 7, 481–531 (2006).
Google Scholar 
Voordouw, G. The genus Desulovibrio: The centennial. Appl. Environ. Microbiol. 61, 2813–2819 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Singaravelen, N., Nee’man, G., Inbar, M. & Izhaki, I. Feeding responses of free-flying honeybees to secondary compounds mimicking floral nectars. J. Chem. Ecol. 31, 2791–2804 (2005).Article 
CAS 

Google Scholar 
Baracchi, D., Marples, A., Jenkins, A. J., Leitch, A. R. & Chittka, L. Nicotine in floral nectar pharmacologically influences bumblebee learning of floral features. Sci. Rep. 7, 1951 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adler, L. S. & Irwin, R. E. Ecological costs and benefits of defenses in nectar. Ecology 86, 2968–2978 (2005).Article 

Google Scholar 
Bally, J. et al. Nicotiana paulineana, a new Australian species in Nicotiana section Suaveolentes. Aust. Syst. Bot. 34, 477–484 (2021).Article 

Google Scholar 
Coenye, T. & Vandamme, P. Diversity and significance of Burkholderia species occupying diverse ecology niches. Environ. Microbiol. 5, 719–729 (2003).CAS 
PubMed 
Article 

Google Scholar 
Levy, A., Merritt, A. J., Aravena-Roman, M., Hodge, M. M. & Inglis, T. J. J. Expanded range of Burkholderia species in Australia. Am. J. Trop. Med. Hyg. 78, 599–604 (2008).CAS 
PubMed 
Article 

Google Scholar 
Kaltenpoth, M. & Flórez, L. V. Versatile and dynamic symbioses between insects and Burkholderia bacteria. Annu. Rev. Entomol. 65, 145–170 (2019).PubMed 
Article 
CAS 

Google Scholar 
Foley, K., Fazio, G., Jensen, A. B. & Hughes, W. O. H. Nutritional limitation and resistance to opportunistic Aspergillus parasites in honey bee larvae. J. Invertebr. Pathol. 111, 68–73 (2012).PubMed 
Article 

Google Scholar 
Yoder, J. A. et al. Fungicide contamination reduces beneficial fungi in bee bread based on an area-wide field study in honey bee, Apis mellifera, colonies. J. Toxicol. Environ. Health Part A 76, 587–600 (2013).CAS 
Article 

Google Scholar 
Yun, J.-H., Jung, M.-J., Kim, P. S. & Bae, J.-W. Social status shapes the bacterial and fungal gut communities of the honey bee. Sci. Rep. 8, 1–11 (2018).
Google Scholar 
Dew, R. M., Silva, D. P. & Rehan, S. M. Range expansion of an already widespread bee under climate change. GECCO 17, e00584 (2019).
Google Scholar 
Cambra, M., Capote, N. & Myrta, A. & Llácer, G. Plum pox virus and the estimated costs associated with sharka disease. EPPO Bull. 36, 202–204 (2006).Article 

Google Scholar 
Roberts, J. M. K., Ireland, K. B., Tay, W. T. & Paini, D. Honey bee-assisted surveillance for early plant virus detection. Ann. Appl. Biol. 173, 285–293 (2018).CAS 
Article 

Google Scholar 
Elliott, B. et al. Pollen diets and niche overlap of honey bees and native bees in protected areas. BAAE 50, 169–180 (2021).
Google Scholar 
Porrini, C. et al. Use of honey bees as bioindicators of environmental pollution in Italy. in Honey bees: estimating the environmental impact of chemicals (eds Devillers, J. & Pham-Delegue, M.-H.) (Taylor & Francis Press, 2002).Kennedy, P., Higginson, A. D., Radford, A. N. & Sumner, S. Altruism in a volatile world. Nature 555, 359–362 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rubin, B. E. R., Sanders, J. G., Turner, K. M., Pierce, N. E. & Kocher, S. D. Social behaviour in bees influences the abundance of Sodalis (Enterobacteriaceae) symbionts. R. Soc. Open Sci. 5, 180369 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Mohr, K. I. & Tebbe, C. C. Diversity and phylotype consistency of bacteria in the guts of three bee species (Apoidea) at an oilseed rape field. Environ. Microbiol. 8, 258–272 (2006).CAS 
PubMed 
Article 

Google Scholar 
Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Curr. Opin. Insect Sci. 26, 97–104 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Amin, F. A. Z. et al. Probiotic properties of Bacillus strains isolated from stingless bee (Heterotrigona itama) honey collected across Malaysia. Int. J. Envrion. Res. Public Health 17, 1–15 (2020).
Google Scholar 
Takeshita, K. & Kikuchi, Y. Riptortus pedestris and Burkholderia symbiont: an ideal model system for insect-microbe symbiotic associations. Res. Microbiol. 168, 175–187 (2017).PubMed 
Article 

Google Scholar 
Martinson, V. G. et al. A simple and distinctive microbiota associated with honey bees and bumble bees. Mol. Ecol. 20, 619–628 (2011).PubMed 
Article 

Google Scholar 
D’Alvise, P. et al. The impact of winter feed type on intestinal microbiota and parasites in honey bees. Apidologie 49, 252–264 (2018).Article 
CAS 

Google Scholar 
Wang, L. et al. Dynamic changes of gut microbial communities of bumble bee queens through important life stages. mSystems 4, e00631–19 (2019).PubMed 
PubMed Central 

Google Scholar 
Kapheim, K. M., Johnson, M. M. & Jolley, M. Composition and acquisition of the microbiome in solitary, ground-nesting alkali bees. Sci. Rep. 11, 2993 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Abdelazez, A. et al. Potential benefits of Lactobacillus plantarum as probiotic and its advantages in human health and industrial applications: A review. Adv. Environ. Biol. 12, 16–27 (2018).CAS 

Google Scholar 
Frese, S. A. et al. The evolution of host specialization in the vertebrate gut symbiont Lactobacillus reuteri. PLoS Genet 7, e1001314 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Duar, R. M. et al. Lifestyles in transition: evolution and natural history of the genus Lactobacillus. FEMS Microbiol. Rev. 41, S27–S48 (2017).PubMed 
Article 

Google Scholar 
Tejerina, M. R., Cabana, M. J. & Benitez-Ahrendts, M. R. Strains of Lactobacillus spp. reduce chalkbrood in Apis mellifera. J. Invertebr. Pathol. 178, 107521 (2021).CAS 
PubMed 
Article 

Google Scholar 
Vásquez, A. et al. Symbionts as major modulators of insect health: Lactic acid bacteria and honeybees. PLOS ONE 7, e33188 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Voulgari-Kokota, A., Steffan-Dewenter, I. & Keller, A. Susceptibility of red mason bee larvae to bacterial threats due to microbiome exchange with imported pollen provisions. Insects 11, 1–14 (2020).Article 

Google Scholar 
Steffan, S. A. et al. Omnivory in bees: Elevated trophic positions among all major bee families. Am. Nat. 194, 414–421 (2019).PubMed 
Article 

Google Scholar 
Hurst, P. S. Social biology of Exoneurella tridentata, an allodapine bee with morphological castes and perennial colonies. Unpublished D. Phil. Thesis (Flinders University, 2001).Chalita, M. et al. Improved metagenomic taxonomic profiling using a curated core gene- based bacterial database reveals unrecognized species in the genus Streptococcus. Pathogens 9, 204 (2021).Article 

Google Scholar 
Rehan, S. M. & Toth, A. L. Climbing the social ladder: molecular evolution of sociality. Trends Ecol. Evol. 30, 426–433 (2015).PubMed 
Article 

Google Scholar 
Shell, W. A. & Rehan, S. M. Behavioral and genetic mechanisms of social evolution: insights from incipiently and facultatively social bees. Apidologie 49, 13–30 (2018).CAS 
Article 

Google Scholar 
Kirby, K. S. Isolation and characterization of ribosomal ribonucleic acid. Biochem. J. 96, 266–269 (1956).Article 

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

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2019).Article 
CAS 

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

Google Scholar 
Tsilimigras, M. C. B. & Fodor, A. A. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann. Epidemiol. 26, 330–335 (2016).PubMed 
Article 

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

Google Scholar 
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Altschul, S. F. et al. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

Google Scholar 
Oksanen, J. et al. Package ‘vegan’. Community Ecology package, version 2, 1–295 (2013).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
Article 

Google Scholar 
Mina, R., Haixu, T. & Yuzhen, Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 38, e191 (2010).Article 
CAS 

Google Scholar 
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).CAS 
PubMed 
Article 

Google Scholar 
Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article 
CAS 

Google Scholar 
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf 9, 599 (2008).Article 
CAS 

Google Scholar 
Langfelder, P. & Horvath, S. Tutorials for the WGCNA package. https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/ (2016).Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
Google Scholar 
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).Article 

Google Scholar 
Paluszynska, A. Structure mining and knowledge extraction from random forest with applications to The Cancer Genome Atlas project. Master’s Thesis (University of Warsaw, 2017). More

Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).ADS 
Article 

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 
Article 

Google Scholar 
Rayner, P. J. et al. Interannual variability of the global carbon cycle (1992-2005) inferred by inversion of atmospheric CO2 and δ13CO2 measurements. Glob. Biogeochem. Cycles 22, 1–12 (2008).Article 
CAS 

Google Scholar 
Piao, S. et al. Interannual variation of terrestrial carbon cycle: Issues and perspectives. Glob. Chang. Biol. 26, 300–318 (2020).ADS 
PubMed 
Article 

Google Scholar 
Betts, R. A. et al. A successful prediction of the record CO2 rise associated with the 2015/2016 El Niño. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170301 (2018).Article 
CAS 

Google Scholar 
Keeling, C. D., Whorf, T. P., Wahlen, M. & van der Plichtt, J. Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980. Nature 375, 666–670 (1995).ADS 
CAS 
Article 

Google Scholar 
Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fang, Y. et al. Global land carbon sink response to temperature and precipitation varies with ENSO phase. Environ. Res. Lett. 12, 064007 (2017).ADS 
Article 

Google Scholar 
Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jung, M. et al. Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature 541, 516–520 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, W. et al. Variations in atmospheric CO2 growth rates coupled with tropical temperature. Proc. Natl Acad. Sci. USA 110, 13061–13066 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, X. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Marcolla, B., Rödenbeck, C. & Cescatti, A. Patterns and controls of inter-annual variability in the terrestrial carbon budget. Biogeosciences 14, 3815–3829 (2017).ADS 
CAS 
Article 

Google Scholar 
Yin, Y. et al. Changes in the response of the northern hemisphere carbon uptake to temperature over the last three decades. Geophys. Res. Lett. 45, 4371–4380 (2018).ADS 
CAS 
Article 

Google Scholar 
Rödenbeck, C., Zaehle, S., Keeling, R. & Heimann, M. How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data. Biogeosciences 15, 2481–2498 (2018).ADS 
Article 
CAS 

Google Scholar 
Palmer, P. I. et al. Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nat. Commun. 10, 1–9 (2019).ADS 
Article 
CAS 

Google Scholar 
Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).CAS 
PubMed 
Article 

Google Scholar 
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hu, L. et al. Enhanced North American carbon uptake associated with El Niño. Sci. Adv. 5, 1–11 (2019).ADS 

Google Scholar 
Liu, Z. et al. Increased high-latitude photosynthetic carbon gain offset by respiration carbon loss during an anomalous warm winter to spring transition. Glob. Chang. Biol. 26, 682–696 (2020).ADS 
PubMed 
Article 

Google Scholar 
Reichstein, M. et al. Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites. Geophys. Res. Lett. 34, 1–5 (2007).Article 

Google Scholar 
Shiga, Y. P. et al. Forests dominate the interannual variability of the North American carbon sink. Environ. Res. Lett. 13, 084015 (2018).ADS 
Article 
CAS 

Google Scholar 
Wang, X., Ciais, P., Wang, Y. & Zhu, D. Divergent response of seasonally dry tropical vegetation to climatic variations in dry and wet seasons. Glob. Chang. Biol. 24, 4709–4717 (2018).ADS 
PubMed 
Article 

Google Scholar 
Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu, J. et al. Detecting drought impact on terrestrial biosphere carbon fluxes over contiguous US with satellite observations. Environ. Res. Lett. 13, 095003 (2018).ADS 
Article 
CAS 

Google Scholar 
Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chevallier, F. et al. Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data. J. Geophys. Res. 110, D24309 (2005).ADS 
Article 
CAS 

Google Scholar 
Rödenbeck, C., Houweling, S., Gloor, M. & Heimann, M. CO2 flux history 1982-2001 inferred from atmospheric data using a global inversion of atmospheric transport. Atmos. Chem. Phys. 3, 1919–1964 (2003).ADS 
Article 

Google Scholar 
Chevallier, F. et al. Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions. Atmos. Chem. Phys. 19, 14233–14251 (2019).ADS 
CAS 
Article 

Google Scholar 
Rödenbeck, C., Zaehle, S., Keeling, R. & Heimann, M. The European carbon cycle response to heat and drought as seen from atmospheric CO2 data for 1999–2018. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190506 (2020).Article 
CAS 

Google Scholar 
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 
Article 

Google Scholar 
Jung, M. et al. Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences 17, 1343–1365 (2020).ADS 
CAS 
Article 

Google Scholar 
Humphrey, V. & Gudmundsson, L. GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth Syst. Sci. Data 11, 1153–1170 (2019).Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhou, S., Zhang, Y., Williams, A. P. & Gentine, P. Projected increases in intensity, frequency, and terrestrial carbon costs of compound drought and aridity events. Sci. Adv. 5, 1–9 (2019).CAS 

Google Scholar 
Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).ADS 
CAS 
Article 

Google Scholar 
Turetsky, M. R. et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–34 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Li, Z. L. et al. Changes in net ecosystem exchange of CO2 in Arctic and their relationships with climate change during 2002–2017. Adv. Clim. Chang. Res. 12, 475–481 (2021).Article 

Google Scholar 
Walker, D. A. et al. The Circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282 (2005).Article 

Google Scholar 
Virkkala, A. M. et al. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob. Chang. Biol. 27, 4040–4059 (2021).PubMed 
Article 

Google Scholar 
Randazzo, N. A. et al. Higher autumn temperatures lead to contrasting CO2 flux responses in boreal forests versus tundra and shrubland. Geophys. Res. Lett. 48, e2021GL093843 (2021).ADS 
CAS 
Article 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B Biol. Sci. 365, 3227–3246 (2010).Article 

Google Scholar 
Piao, S. et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Chang. 7, 359–363 (2017).ADS 
CAS 
Article 

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

Google Scholar 
Tramontana, G. et al. Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences 13, 4291–4313 (2016).ADS 
CAS 
Article 

Google Scholar 
Randerson, J. T., Field, C. B., Fung, I. Y. & Tans, P. P. Increases in early season ecosystem uptake explain recent changes in the seasonal cycle of atmospheric CO2 at high northern latitudes. Geophys. Res. Lett. 26, 2765–2768 (1999).ADS 
CAS 
Article 

Google Scholar 
Black, T. A. et al. Increased carbon sequestration by a boreal deciduous forest in years with a warm spring. Geophys. Res. Lett. 27, 1271–1274 (2000).ADS 
Article 

Google Scholar 
Wang, T. et al. Emerging negative impact of warming on summer carbon uptake in northern ecosystems. Nat. Commun. 9, 1–7 (2018).ADS 
Article 
CAS 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Seneviratne, S. I. et al. Investigating soil moisture-climate interactions in a changing climate: a review. Earth-Sci. Rev. 99, 125–161 (2010).ADS 
CAS 
Article 

Google Scholar 
Buermann, W. et al. Recent shift in Eurasian boreal forest greening response may be associated with warmer and drier summers. Geophys. Res. Lett. 41, 1995–2002 (2014).ADS 
Article 

Google Scholar 
Fu, R. et al. Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proc. Natl Acad. Sci. USA 110, 18110–18115 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, C. et al. Identifying critical climate periods for vegetation growth in the northern hemisphere. J. Geophys. Res. Biogeosci. 123, 2541–2552 (2018).Article 

Google Scholar 
Gloor, E. et al. Tropical land carbon cycle responses to 2015/16 El Niño as recorded by atmospheric greenhouse gas and remote sensing data. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170302 (2018).Article 
CAS 

Google Scholar 
Saatchi, S. et al. Detecting vulnerability of humid tropical forests to multiple stressors. One Earth 4, 988–1003 (2021).ADS 
Article 

Google Scholar 
Peylin, P. et al. Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions. Biogeosciences 10, 6699–6720 (2013).ADS 
CAS 
Article 

Google Scholar 
Liu, J. et al. Carbon monitoring system flux net biosphere exchange 2020 (CMS-Flux NBE 2020). Earth Syst. Sci. Data 13, 299–330 (2021).ADS 
Article 

Google Scholar 
Quetin, G. R., Bloom, A. A., Bowman, K. W. & Konings, A. G. Carbon flux variability from a relatively simple ecosystem model with assimilated data is consistent with terrestrial biosphere model estimates. J. Adv. Model. Earth Syst. 12, e2019MS001889 (2020).ADS 
Article 

Google Scholar 
Schewe, J. et al. State-of-the-art global models underestimate impacts from climate extremes. Nat. Commun. 10, 1005 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gentine, P. et al. Coupling between the terrestrial carbon and water cycles – A review. Environ. Res. Lett. 14, 83003 (2019).CAS 
Article 

Google Scholar 
Bastos, A. et al. Impacts of extreme summers on European ecosystems: a comparative analysis of 2003, 2010 and 2018. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190507 (2020).CAS 
Article 

Google Scholar 
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).ADS 
CAS 
Article 

Google Scholar 
Melton, J. R. & Arora, V. K. Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0. Geosci. Model Dev. 9, 323–361 (2016).ADS 
CAS 
Article 

Google Scholar 
Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. 11, 4245–4287 (2019).ADS 
Article 

Google Scholar 
Tian, H. et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Clim. Change 129, 413–426 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Meiyappan, P., Jain, A. K. & House, J. I. Increased influence of nitrogen limitation on CO2 emissions from future land use and land use change. Glob. Biogeochem. Cycles 29, 1524–1548 (2015).ADS 
CAS 
Article 

Google Scholar 
Mauritsen, T. et al. Developments in the MPI‐M Earth system model version 1.2 (MPI‐ESM1.2) and its response to increasing CO2. J. Adv. Model. Earth Syst. 11, 998–1038 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poulter, B., Frank, D. C., Hodson, E. L. & Zimmermann, N. E. Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO2 airborne fraction. Biogeosciences 8, 2027–2036 (2011).ADS 
CAS 
Article 

Google Scholar 
Lienert, S. & Joos, F. A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions. Biogeosciences 15, 2909–2930 (2018).ADS 
CAS 
Article 

Google Scholar 
Zaehle, S. & Friend, A. D. Carbon and nitrogen cycle dynamics in the O-CN land surface model: 1. Model description, site-scale evaluation, and sensitivity to parameter estimates. Glob. Biogeochem. Cycles 24, 1–13 (2010).
Google Scholar 
Goll, D. S. et al. A representation of the phosphorus cycle for ORCHIDEE (revision 4520). Geosci. Model Dev. 10, 3745–3770 (2017).ADS 
CAS 
Article 

Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, 1–33 (2005).Article 
CAS 

Google Scholar 
Walker, A. P. et al. The impact of alternative trait‐scaling hypotheses for the maximum photosynthetic carboxylation rate (Vcmax) on global gross primary production. N. Phytol. 215, 1370–1386 (2017).CAS 
Article 

Google Scholar 
Joetzjer, E. et al. Improving the ISBACC land surface model simulation of water and carbon fluxes and stocks over the Amazon forest. Geosci. Model Dev. 8, 1709–1727 (2015).ADS 
Article 

Google Scholar 
Kato, E., Kinoshita, T., Ito, A., Kawamiya, M. & Yamagata, Y. Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model. J. Land Use Sci. 8, 104–122 (2013).Article 

Google Scholar 
Wei, Y. et al. The North American carbon program multi-scale synthesis and terrestrial model intercomparison project – Part 2: environmental driver data. Geosci. Model Dev. 7, 2875–2893 (2014).ADS 
Article 

Google Scholar 
Dlugokencky, E. J., Thoning, K. W., Lang, P. M. & Tans, P. P. NOAA greenhouse gas reference from atmospheric carbon dioxide dry air mole fractions from the NOAA ESRL carbon cycle cooperative global air sampling network. ftp://aftp.cmdl.noaa.gov/data/trace_gases/co2/flask/surface/ (2017). More

Zhu, Y. G. et al. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat. Microbiol. 2, 16270, https://doi.org/10.1038/nmicrobiol.2016.270 (2017).CAS 
Article 
PubMed 

Google Scholar 
Hutchins, D. A. & Fu, F. Microorganisms and ocean global change. Nat. Microbiol. 2, 17058, https://doi.org/10.1038/nmicrobiol.2017.58 (2017).CAS 
Article 
PubMed 

Google Scholar 
Wang, J. & Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14, 508–522, https://doi.org/10.1038/nrmicro.2016.83 (2016).CAS 
Article 
PubMed 

Google Scholar 
Kan, J., Suzuki, M. T., Wang, K., Evans, S. E. & Chen, F. High temporal but low spatial heterogeneity of bacterioplankton in the Chesapeake bay. Appl. Environ. Microbiol. 73, 6776–6789, https://doi.org/10.1128/Aem.00541-07 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bouvier, T. C. & del Giorgio, P. A. Compositional changes in free-living bacterial communities along a salinity gradient in two temperate estuaries. Limnol. Oceanogr. 47, 453–470, https://doi.org/10.4319/lo.2002.47.2.0453 (2002).ADS 
CAS 
Article 

Google Scholar 
Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220, https://doi.org/10.1038/ismej.2012.93 (2013).CAS 
Article 
PubMed 

Google Scholar 
Fortunato, C. S., Herfort, L., Zuber, P., Baptista, A. M. & Crump, B. C. Spatial variability overwhelms seasonal patterns in bacterioplankton communities across a river to ocean gradient. ISME J. 6, 554–563, https://doi.org/10.1038/ismej.2011.135 (2012).CAS 
Article 
PubMed 

Google Scholar 
Ghosh, A. & Bhadury, P. Exploring biogeographic patterns of bacterioplankton communities across global estuaries. MicrobiologyOpen 8, https://doi.org/10.1002/mbo3.741 (2019).Zhang, C. J., Chen, Y. L., Pan, J., Wang, Y. M. & Li, M. Spatial and seasonal variation of methanogenic community in a river-bay system in South China. Appl. Microbiol. Biotechnol. 104, 4593–4603, https://doi.org/10.1007/s00253-020-10613-z (2020).CAS 
Article 
PubMed 

Google Scholar 
Yu, T. et al. Characteristics of Microbial Communities and Their Correlation With Environmental Substrates and Sediment Type in the Gas-Bearing Formation of Hangzhou Bay, China. Front. Microbiol. 10, https://doi.org/10.3389/fmicb.2019.02421 (2019).Zhou, L. et al. Stochastic determination of the spatial variation of potentially pathogenic bacteria communities in a large subtropical river. Environ. Pollut. 264, 114683, https://doi.org/10.1016/j.envpol.2020.114683 (2020).CAS 
Article 
PubMed 

Google Scholar 
Zhou, L. et al. Environmental filtering dominates bacterioplankton community assembly in a highly urbanized estuarine ecosystem. Environ. Res. 196, 110934, https://doi.org/10.1016/j.envres.2021.110934 (2021).CAS 
Article 
PubMed 

Google Scholar 
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676, https://doi.org/10.1093/bioinformatics/btv033 (2015).CAS 
Article 
PubMed 

Google Scholar 
Mikheenko, A., Saveliev, V. & Gurevich, A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 32, 1088–1090, https://doi.org/10.1093/bioinformatics/btv697 (2016).CAS 
Article 
PubMed 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, https://doi.org/10.1186/s40168-018-0541-1 (2018).Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A. & Korobeynikov, A. Using SPAdes de novo assembler. Curr. Protoc. Bioinf. 70, e102, https://doi.org/10.1002/cpbi.102 (2020).CAS 
Article 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication[J]. ISME J. 11, 2864–2868, https://doi.org/10.1038/ismej.2017.126 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Uritskiy, G. et al. Halophilic microbial community compositional shift after a rare rainfall in the Atacama Desert. ISME J. 13, 2737–2749, https://doi.org/10.1038/s41396-019-0468-y (2019).CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927, https://doi.org/10.1093/bioinformatics/btz848 (2020).CAS 
Article 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086, https://doi.org/10.1038/s41587-020-0501-8 (2020).CAS 
Article 
PubMed 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PloS one 5, e9490, https://doi.org/10.1371/journal.pone.0009490 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

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).CAS 
Article 
PubMed 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296, https://doi.org/10.1093/nar/gkab301 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRP320016 (2021).Zhou, L., Huang, S., Gong, J., Xu, P. & Huang, X. 500 metagenome-assembled microbial genomes from 30 subtropical estuaries in South China. Figshare https://doi.org/10.6084/m9.figshare.14717061.v4 (2021).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055, https://doi.org/10.1101/gr.186072.114 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Panti-May, J. A. et al. A two-year ecological study of Norway rats (Rattus norvegicus) in a Brazilian Urban Slum. PLoS ONE 11(3), 1–12. https://doi.org/10.1371/journal.pone.0152511 (2016).CAS 
Article 

Google Scholar 
Himsworth, C. G. et al. A mixed methods approach to exploring the relationship between Norway rat (Rattus norvegicus) abundance and features of the urban environment in an inner-city neighborhood of Vancouver, Canada. PLoS ONE 9(5), 97776. https://doi.org/10.1371/journal.pone.0097776 (2014).ADS 
CAS 
Article 

Google Scholar 
Lambert, M. S., Quy, R. J., Smith, R. H. & Cowan, D. P. The effect of habitat management on home-range size and survival of rural Norway rat populations. J. Appl. Ecol. 45(6), 1753–1761. https://doi.org/10.1111/j.1365-2664.2008.01543.x (2008).Article 

Google Scholar 
Meerburg, B. G., Singleton, G. R. & Kijlstra, A. Rodent-borne diseases and their risks for public health (Vol. 7828). https://doi.org/10.1080/10408410902989837 (2009)Buckle, A. & Smith, R. Rodent Pests and Their Control 2nd edn. (CABI Press, Wallingford, 2015).Book 

Google Scholar 
Byers, K. A., Lee, M. J., Patrick, D. M. & Himsworth, C. G. Rats about town: A systematic review of rat movement in urban ecosystems. Front. Ecol. Evol. 7, 1–12. https://doi.org/10.3389/fevo.2019.00013 (2019).Article 

Google Scholar 
Carvalho-Pereira, T. et al. The helminth community of a population of Rattus norvegicus from an urban Brazilian slum and the threat of zoonotic diseases. Parasitology 145(6), 797–806. https://doi.org/10.1017/S0031182017001755 (2018).Article 
PubMed 

Google Scholar 
Costa, F. et al. Patterns in Leptospira shedding in Norway rats (Rattus norvegicus) from Brazilian slum communities at high risk of disease transmission. PLoS Negl. Trop. Dis. 9(6), 1–14. https://doi.org/10.1371/journal.pntd.0003819 (2015).CAS 
Article 

Google Scholar 
Parsons, M. H. et al. Rats and the COVID-19 pandemic: Early data on the global emergence of rats in response to social distancing. MedRxiv https://doi.org/10.1101/2020.07.05.20146779 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Awoniyi, A. M. et al. Effect of chemical and sanitary intervention on rat sightings in urban communities of New Providence, the Bahamas. SN Appl. Sci. 3, 495. https://doi.org/10.1007/s42452-021-04459-x (2021).CAS 
Article 

Google Scholar 
Costa, F. et al. Influence of household rat infestation on leptospira transmission in the urban slum environment. PLoS Negl. Trop. Dis. 8(12), 3338. https://doi.org/10.1371/journal.pntd.0003338 (2014).Article 

Google Scholar 
Khalil, H. et al. Poverty, sanitation, and Leptospira transmission pathways in residents from four Brazilian slums. PLoS Negl. Trop. Dis. 15(3), 1–15. https://doi.org/10.1371/journal.pntd.0009256 (2021).Article 

Google Scholar 
Zeppelini, C. G. et al. Demographic drivers of Norway rat populations from urban slums in Brazil. Urban Ecosyst. https://doi.org/10.1007/s11252-020-01075-2 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
United Nations -UN. World Urbanization Prospects: The 2018 Revision. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html. Accessed 24 Dec 2020 (2018)United Nations UN-SDG. Sustainable Development Goals: Make cities and human settlements inclusive, safe, resilient and sustainable. https://unstats.un.org/sdgs/report/2019/goal-11/#:~:text=The%20absolute%20number%20of%20people,Southern%20Asia%20(227%20million). Accessed 24 Dec 2020 (2018)Russell, J. C., Towns, D. R. & Clout, M. N. Review of rat invasion biology: Implications for island biosecurity. Sci. Conserv. 286, 1–53 (2008).
Google Scholar 
Minter, A. et al. Optimal control of rat-borne leptospirosis in an urban environment. Front. Ecol. Evol. 7, 1–10. https://doi.org/10.3389/fevo.2019.00209 (2019).ADS 
Article 

Google Scholar 
Mathur, R. P. Effectiveness of various rodent control measures in cereal crops and plantations in India. In: Leirs H. and Schockaert E. ed. Proceedings of the International Workshop on Rodent Biology and Integrated Pest Management in Africa, 21-25 October 1996, Morogoro, Tanzania. Belg. J. Zool. 127(supplement 1), 137–144 (1997).
Google Scholar 
Pascal, M., Siorat, F., Lorvelec, O., Yésou, P. & Simberloff, D. A pleasing consequence of Norway rat eradication : Two shrew species recover. Divers. Distrib. 11, 193–198. https://doi.org/10.1111/j.1366-9516.2005.00137.x (2005).Article 

Google Scholar 
Singleton, G. R., Hinds, L. & Leirs, H. Ecologically-based management of rodent pests. Australian Centre for International Agricultural Research, (ACIAR Monograph 59), 494. (1999)Sullivan, L. M. Roof rat control around homes and other structures. Cooper. Extens. Bull. AZ 1280, 1–6 (2002).
Google Scholar 
Childs, J. E. Size-dependent predation on rats (Rattus norvegicus) by house cats (Felis catus) in an urban setting. J. Mammol. 67(1), 196–199 (1986).Article 

Google Scholar 
Davis, D. E. The characteristics of rat populations. Quart. Rev. Biol. 28, 373–401. https://doi.org/10.1086/399860 (1953).CAS 
Article 
PubMed 

Google Scholar 
Glass, G. E. et al. Trophic garnishes: Cat-Rat interactions in an urban environment. PLoS ONE 4(6), e5794. https://doi.org/10.1371/journal.pone.0005794 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lenton, G. M. Biological control of rats by owls in oil palm and other plantations. Biotrop Spec. Publ. 12, 87–94 (1980).
Google Scholar 
Smith, R. H. & Meyer, A. N. Rodent controlmethods: Non-chemical and non-lethal chemical, with special reference to food stores. In Rodent Pests and Their Control 2nd edn (eds Buckle, A. & Smith, R.) 81–101 (CABI International, 2015) (ISBN-13: 978-1-84593-817-8).
Google Scholar 
Oyedele, D. T., Sah, S. A. M., Kairuddin, L. & Ibrahim, W. M. M. W. Range measurement and a habitat suitability map for the Norway rat in a highly developed urban environment. Trop. Life Sci. Res. 26(2), 27–44 (2015).PubMed 
PubMed Central 

Google Scholar 
Hansen, N., Hughes, N. K., Bryom, A. E. & Banks, P. B. Population recovery of alien black rats Rattus rattus: A test of reinvasion theory. Austral Ecol. 45, 291–304. https://doi.org/10.1111/aec.12855 (2020).Article 

Google Scholar 
Awoniyi, A. M. et al. Using Rhodamine B to assess the movement of small mammals in an urban slum. Methods Ecol. Evol. 12(11), 2234–2242. https://doi.org/10.1111/2041-210X.13693 (2021).Article 

Google Scholar 
Glass, G. E., Klein, S. L., Norris, D. E. & Gardner, L. C. Multiple paternity in urban Norway rats: Extended ranging for mates. Vector-Borne Zoonotic Dis. 16(5), 342–248. https://doi.org/10.1089/vbz.2015.1816 (2016).Article 
PubMed 

Google Scholar 
Buckle, A. P. & Eason, C. T. Rodent control methods: Chemical. In Rodent Pests and Their Control 2nd edn (eds Buckle, A. & Smith, R.) 81–101 (CABI International, Wallingford, 2015) (ISBN-13: 978-1-84593-817-8).Chapter 

Google Scholar 
de Masi, E., Pedro, J. V. & Maria, T. P. Evaluation on the effectiveness of actions for controlling infestation by rodents in Campo Limpo region, São Paulo Municipality, Brazil Access details: Access Details: [subscription number 913003116]. Int. J. Environ. Health Res. 19(4), 291–304. https://doi.org/10.1080/09603120802592723 (2009).Article 
PubMed 

Google Scholar 
Lambropoulos, A. S. et al. Rodent control in urban areas—An interdisciplinary approach. J. Environ. Health 61, 12–17 (1999).
Google Scholar 
Reis, R. B. et al. Impact of environment and social gradient on Leptospira infection in urban slums. PLoS Negl. Trop. Dis. 2(4), 11–18. https://doi.org/10.1371/journal.pntd.0000228 (2008).MathSciNet 
Article 

Google Scholar 
Instituto Brasileiro de Geografia e Estatistica (IBGE). Accessed 15 November 2019 (2010)CDC. Integrated pest management: conducting urban rodent surveys. Centers for Disease Control and Prevention-Atlanta: US Department of Health and Human Services (2006)Hacker, K. P. et al. A comparative assessment of track plates to quantify fine scale variations in the relative abundance of Norway rats in urban slums. Urban Ecosyst. 19(2), 561–575. https://doi.org/10.1007/s11252-015-0519-8 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Eyre, M. T. et al. A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community: A multivariate geostatistical framework for combining multiple ind. J. R. Soc. Interface 17(170), 1–21. https://doi.org/10.1098/rsif.2020.0398 (2020).Article 

Google Scholar 
Bursac, Z., Gauss, C. H., Williams, D. K. & Hosmer, D. W. Purposeful selection of variables in logistic regression. Source Code Biol. Med. 8, 1–8. https://doi.org/10.1186/1751-0473-3-17 (2008).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach (Springer, 2002).MATH 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org (2019).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020)Kassambara A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr (2020)Richardson, J. L. et al. Using fine-scale spatial genetics of Norway rats to improve control efforts and reduce leptospirosis risk in urban slum environments. Evol. Appl. 10(4), 323–337. https://doi.org/10.1111/eva.12449 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Santos, N. D. J., Sousa, E., Reis, M. G., Ko, A. I. & Costa, F. Rat infestation associated with environmental deficiencies in an urban slum community with high risk of leptospirosis. Cad. Saúde Pública 33(2), 1–13. https://doi.org/10.1590/0102-311X00132115 (2017).CAS 
Article 

Google Scholar 
Murray, M. H. & Sanchez, C. A. Urban rat exposure to anticoagulant rodenticides and zoonotic infection risk. Biol. Lett. 17, 20210311. https://doi.org/10.1098/rsbl.2021.0311 (2021).CAS 
Article 
PubMed 

Google Scholar 
Parsons, M. H., Banks, P. B., Deutsch, M. A., Corrigan, R. F. & Munshi-South, J. Trends in urban rat ecology: A framework to define the prevailing knowledge gaps and incentives for academia, pest management professionals (PMPs) and public health agencies to participate. J. Urban Ecol. 3(1), 1–8. https://doi.org/10.1093/jue/jux005 (2017).Article 

Google Scholar 
Costa, F. et al. Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis. PLoS Negl. Trop. Dis. 15(3), 9154. https://doi.org/10.1371/journal.pntd.0009154 (2021).Article 

Google Scholar 
Mwanjabe, P. S. & Leirs, H. An early warning system for IPM-based rodent control in smallholder farming systems in Tanzania. In: Leirs, H., & Schockaert, E., ed., Proceedings of the International Workshop on Rodent Biology and Integrated Pest Management in Africa, 21-25 October 1996, Morogoro, Tanzania. Belg. J. Zool. 127(supplement 1), 4–58 (1997).
Google Scholar 
Richards, C. G. J. R. & Buckle, A. P. Towards integrated rodent pest management at the village level. In Control of Mammal Pests (eds Richards, C. G. J. R. & Ku, T. Y.) 293–312 (Taylor and Francis, 1987).
Google Scholar 
Masi, E. Socioeconomic and environmental risk factors for urban rodent infestation in Sao Paulo, Brazil. J. Pest Sci. 83(3), 231–241. https://doi.org/10.1007/s10340-010-0290-9 (2010).Article 

Google Scholar 
Brooks, J. E. Methods of sewer rat control. In Proceedings of the 1st Vertebrate Pest Conference. https://digitalcommons.unl.edu/vpcone/17. Accessed 20 August 2021 (1962) More

Gattuso, J.-P. et al. Ocean solutions to address climate change and its effects on marine ecosystems. Front. Mar. Sci. 5, 337 (2018).Article 

Google Scholar 
Pauly, D. The gill-oxygen limitation theory (GOLT) and its critics. Sci. Adv. 7, 6050 (2021).Article 
ADS 
CAS 

Google Scholar 
Miller, D. D., Ota, Y., Sumaila, U. R., Cisneros-Montemayor, A. M. & Cheung, W. W. L. Adaptation strategies to climate change in marine systems. Glob. Change Biol. 24, e1–e14 (2018).Article 
ADS 

Google Scholar 
Chan, F. T. et al. Climate change opens new frontiers for marine species in the Arctic: Current trends and future invasion risks. Glob. Change Biol. 25, 25–38 (2019).Article 
ADS 

Google Scholar 
Cheung, W. W. L. et al. Structural uncertainty in projecting global fisheries catches under climate change. Ecol. Model. 325, 57–66 (2016).CAS 
Article 

Google Scholar 
Pita, I., Mouillot, D., Moullec, F. & Shin, Y. Contrasted patterns in climate change risk for Mediterranean fisheries. Glob. Change Biol. 27, 5920–5933 (2021).Article 

Google Scholar 
Tacon, A. G. J. & Metian, M. Fishing for aquaculture: Non-food use of small pelagic forage fish—a global perspective. Rev. Fish. Sci. 17, 305–317 (2009).Article 

Google Scholar 
Coll, M., Pennino, M. G., Steenbeek, J., Sole, J. & Bellido, J. M. Predicting marine species distributions: Complementarity of food-web and Bayesian hierarchical modelling approaches. Ecol. Model. 405, 86–101 (2019).Article 

Google Scholar 
Schickele, A. et al. Improving predictions of invasive fish ranges combining functional and ecological traits with environmental suitability under climate change scenarios. Glob. Change Biol. 27, 6086–6102 (2021).Article 

Google Scholar 
Lejeusne, C., Chevaldonné, P., Pergent-Martini, C., Boudouresque, C. F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25, 250–260 (2010).PubMed 
Article 

Google Scholar 
Cramer, W. et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Change 8, 972–980 (2018).Article 
ADS 

Google Scholar 
FAO. The State of Mediterranean and Black Sea Fisheries 2020—At a glance. 20 (2020).McGinty, N., Barton, A. D., Finkel, Z. V., Johns, D. G. & Irwin, A. J. Niche conservation in copepods between ocean basins. Ecography https://doi.org/10.1111/ecog.05690 (2021).Article 

Google Scholar 
Dormann, C. F. et al. Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Glob. Ecol. Biogeogr. 27, 1004–1016 (2018).Article 

Google Scholar 
Hannemann, H., Willis, K. J. & Macias-Fauria, M. The devil is in the detail: unstable response functions in species distribution models challenge bulk ensemble modelling: Unstable response functions in SDMs. Glob. Ecol. Biogeogr. 25, 26–35 (2016).Article 

Google Scholar 
Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Chang. 9, 237–243 (2019).Article 
ADS 

Google Scholar 
Lasram, B. R. et al. An open-source framework to model present and future marine species distributions at local scale. Ecol. Inform. 59, 101130 (2020).Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, 4858 (2019).Article 
ADS 

Google Scholar 
Schickele, A. et al. European small pelagic fish distribution under global change scenarios. Fish Fish 22, 212–225 (2021).Article 

Google Scholar 
Duarte, R., Azevedo, M., Landa, J. & Pereda, P. Reproduction of angler®sh (Lophius budegassa Spinola and Lophius piscatorius Linnaeus) from the Atlantic Iberian coast. Fish. Res. 13, 2 (2001).
Google Scholar 
Nunes, P., Svensson, L. & Markandya, A. Handbook on the Economics and Management of Sustainable Oceans (Edward Elgar Publishing, 2017).Book 

Google Scholar 
Schickele, A. et al. Modelling European small pelagic fish distribution: Methodological insights. Ecol. Model. 416, 108902 (2020).Article 

Google Scholar 
Cheung, W. W. L., Jones, M. C., Reygondeau, G. & Frölicher, T. L. Opportunities for climate-risk reduction through effective fisheries management. Glob. Change Biol. 24, 5149–5163 (2018).Article 
ADS 

Google Scholar 
Bossier, S. et al. The Baltic Sea Atlantis: An integrated end-to-end modelling framework evaluating ecosystem-wide effects of human-induced pressures. PLoS ONE 13, e0199168 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Valle, C., Bayle-Sempere, J. T., Dempster, T., Sanchez-Jerez, P. & Giménez-Casalduero, F. Temporal variability of wild fish assemblages associated with a sea-cage fish farm in the south-western Mediterranean Sea. Estuar. Coast. Shelf Sci. 72, 299–307 (2007).Article 
ADS 

Google Scholar 
Madurell, T., Cartes, J. E. & Labropoulou, M. Changes in the structure of fish assemblages in a bathyal site of the Ionian Sea (eastern Mediterranean). Fish. Res. 66, 245–260 (2004).Article 

Google Scholar 
Volkoff, H. & Rønnestad, I. Effects of temperature on feeding and digestive processes in fish. Temperature 7, 307–320 (2020).Article 

Google Scholar 
Rutterford, L. A. et al. Future fish distributions constrained by depth in warming seas. Nat. Clim. Change 5, 569–573 (2015).Article 
ADS 

Google Scholar 
Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).CAS 
Article 
ADS 

Google Scholar 
Conti, L. & Scardi, M. Fisheries yield and primary productivity in large marine ecosystems. Mar. Ecol. Prog. Ser. 410, 233–244 (2010).Article 
ADS 

Google Scholar 
Chérif, M. et al. Food and feeding habits of the red mullet, Mullus barbatus (Actinopterygii: Perciformes: Mullidae), off the northern Tunisian coast (central Mediterranean). Acta Icth et Piscat 41, 109–116 (2011).Article 

Google Scholar 
Mellon-Duval, C. et al. Trophic ecology of the European hake in the Gulf of Lions, northwestern Mediterranean Sea. Sci. Mar. 81, 7 (2017).Article 

Google Scholar 
Steingrund, P. & Gaard, E. Relationship between phytoplankton production and cod production on the Faroe Shelf. ICES J. Mar. Sci. 62, 163–176 (2005).Article 

Google Scholar 
Friedland, K. D. et al. Pathways between primary production and fisheries yields of large marine ecosystems. PLoS ONE 7, e28945 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Frederiksen, M., Edwards, M., Richardson, A. J., Halliday, N. C. & Wanless, S. From plankton to top predators: Bottom-up control of a marine food web across four trophic levels. J. Anim. Ecol. 75, 1259–1268 (2006).PubMed 
Article 

Google Scholar 
Vasilakopoulos, P., Raitsos, D. E., Tzanatos, E. & Maravelias, C. D. Resilience and regime shifts in a marine biodiversity hotspot. Sci. Rep. 7, 13647 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Issifu, I., Alava, J. J., Lam, V. W. Y. & Sumaila, U. R. Impact of ocean warming, overfishing and mercury on European fisheries: A risk assessment and policy solution framework. Front. Mar. Sci. 8, 770805 (2022).Article 

Google Scholar 
Lima, A. R. A. et al. Forecasting shifts in habitat suitability across the distribution range of a temperate small pelagic fish under different scenarios of climate change. Sci. Total Environ. 804, 150167 (2022).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Sumaila, U. R. et al. Benefits of the Paris Agreement to ocean life, economies, and people. Sci. Adv. 5, 3855 (2019).Article 
ADS 

Google Scholar 
Holsman, K. K. et al. Ecosystem-based fisheries management forestalls climate-driven collapse. Nat. Commun. 11, 4579 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Sumaila, U. R. & Tai, T. C. End overfishing and increase the resilience of the ocean to climate change. Front. Mar. Sci. 7, 523 (2020).Article 

Google Scholar 
Lindegren, M. & Brander, K. Adapting fisheries and their management to climate change: A review of concepts, tools, frameworks, and current progress toward implementation. Rev. Fish. Sci. Aquacult. 26, 400–415 (2018).Article 

Google Scholar 
Demirel, N., Zengin, M. & Ulman, A. First large-scale eastern mediterranean and black sea stock assessment reveals a dramatic decline. Front. Mar. Sci. 7, 103 (2020).Article 

Google Scholar 
Weiss, C. V. C. et al. Climate change effects on marine renewable energy resources and environmental conditions for offshore aquaculture in Europe. ICES J. Mar. Sci. 77, 3168–3182 (2020).Article 

Google Scholar 
Cascarano, M. C. et al. Mediterranean aquaculture in a changing climate: temperature effects on pathogens and diseases of three farmed fish species. Pathogens 10, 1205 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Kleitou, P. et al. Fishery reforms for the management of non-indigenous species. J. Environ. Manag. 280, 111690 (2021).Article 

Google Scholar 
Hamida, B.-B. & O, Ben Hadj Hamida N, Chaouch H, Missaoui H,. Allometry, condition factor and growth of the swimming blue crab Portunus segnis in the Gulf of Gabes, Southeastern Tunisia (Central Mediterranean). Medit. Mar. Sci. 20, 566 (2019).Article 

Google Scholar 
Wisz, M. S. et al. Reply to ‘Sources of uncertainties in cod distribution models’. Nat. Clim. Change 5, 790–791 (2015).Article 
ADS 

Google Scholar 
Kramer-Schadt, S. et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366–1379 (2013).Article 

Google Scholar 
Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Glob. Change Biol. 16, 1145–1157 (2010).Article 
ADS 

Google Scholar 
Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558 (2020).Article 

Google Scholar 
Thuiller, W., Damie, G., Robin, E., Frank, F.Biomod2: Ensemble Platform for Species Distribution Modeling (2016).Stolar, J. & Nielsen, S. E. Accounting for spatially biased sampling effort in presence-only species distribution modelling. Divers. Distrib. 21, 595–608 (2015).Article 

Google Scholar 
Stockwell, D. The GARP modelling system: Problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158 (1999).Article 

Google Scholar 
Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: Convex hull volume. Ecology 87(6), 1465–1471 (2003).Article 

Google Scholar 
Hengl, T., Sierdsema, H., Radović, A. & Dilo, A. Spatial prediction of species’ distributions from occurrence-only records: Combining point pattern analysis ENFA and regression-kriging. Ecol. Modell. 220, 3499–3511 (2009).Article 

Google Scholar 
Faillettaz, R., Beaugrand, G., Goberville, E. & Kirby, R. R. Atlantic Multidecadal Oscillations drive the basin-scale distribution of Atlantic bluefin tuna. Sci. Adv. 5, eaar6993 (2019).Lavoie, D., Lambert, N. & Gilbert, D. Projections of future trends in biogeochemical conditions in the northwest Atlantic using CMIP5 earth system models. Atmos. Ocean 57, 18–40 (2019).CAS 
Article 

Google Scholar 
Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106, 7183–7192 (2001).Article 
ADS 

Google Scholar 
Cristofari, R. et al. Climate-driven range shifts of the king penguin in a fragmented ecosystem. Nat. Clim. Change 8, 245–251 (2018).Article 
ADS 

Google Scholar 
Zeller, D. et al. Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility. Mar. Policy 70, 145–152 (2016).Article 

Google Scholar 
GBIF.org (27 May 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.2crvdpGBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.y8ujd7GBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.hs8py7GBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.kqwq3aGBIF.org (14 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.raka7jGBIF.org (14 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.fwbk43GBIF.org (30 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.845mcwGBIF.org (30 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.wdavbrGBIF.org (11 September 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.ucuavw More