Ecology

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Sauterey, B. et al. Early Mars habitability and global cooling by H2-based methanogens. Nat. Astron. https://doi.org/10.1038/s41550-022-01786-w (2022). More

npj Biodiversity aims to be a common forum where discoveries in all areas of biodiversity science can be discussed, so that the research in specific topics with broad implications for other disciplines permeates the whole community. This requires that scientific debates are made in egalitarian terms between people with different backgrounds and points of view. We will strive to provide safe spaces where all biodiversity research can be showcased without bias, and theoretical and practical advances can be subject to calm and civil debate. As journal editors we will implement measures to work towards a fairer and more inclusive science, such as giving proper recognition to all researchers involved in the research published13, or ensuring in revisions that former research made by different identity groups and local scientists is adequately acknowledged14. We will also acknowledge diversity by maintaining a diverse editorial board15 and engaging external peer-reviewers16 that represent local specialists, the diversity of approaches in each field, as well as early-career researchers across demographic groups. We will also encourage access to research and engage in the FAIR principles for data management and sharing17. Here, good practice includes making data available for reanalysis or compilation in larger databases by researchers anywhere in the world, promoting open software, and sharing reproducible code18,19. Our hope is that this extends the capacity of developing meta-analyses and macroecological and macroevolutionary research beyond the borders of high-income countries.npj Biodiversity seeks to promote scientific discussion and synthesis. As editors, we will act as guides and moderators rather than as gatekeepers that merely decide which papers are above the threshold of publication20. Thus, we encourage debate as a central part of the editorial process, allowing well-grounded and clearly-identified speculation and policy-related statements in published papers when appropriate. This may include publishing non-conventional papers that foster discussion in established topics or open new research avenues21, if and only if they are well supported by data or published evidence. In this sense, we welcome Comments on areas currently under discussion, as well as Reviews and Perspectives that allow synthesis in theoretical and practical topics that are not necessarily general, but can help advance specific subdisciplines or topics. Last but not least, we want to facilitate communication between basic research and applied practitioners through Perspectives that translate the implications of recent research for management, conservation and adaptation to global change, or that identify which theoretical advances or additional empirical evidence would be needed to tackle specific problems.Creating the appropriate publishing environment for journals to be true forums for debate and provide value to the scientific community is a challenging enterprise. Above all, it requires escaping from the haste imposed by the “publish or perish model”, and making an explicit effort to raise the quality of the editorial process. In npj Biodiversity we will seek to follow ‘slow publishing’ principles, putting emphasis on meaningful debate between authors, editors and reviewers22. Current research environments can prevent researchers from having time to think, but true advance stems from digesting ideas and discussing them with the detail, depth and time they may need (http://slow-science.org/)23,24,25. Therefore, to contribute to a healthier, gentler and more thoughtful approach to biodiversity science, we will provide thorough and thoughtful reviews. We will make editorial decisions that, when paired with equally thorough and thoughtful work by authors, can reduce the number of times a paper bounces back and forth in successive rounds of peer review and revision. Note that this does not necessarily mean longer editorial times! Paradoxically, when authors, reviewers and editors commit to these “slow” publishing principles, the publication process can speed up. And most importantly, it will promote the spirit of productive debate that we aim for in npj Biodiversity. More

To our knowledge, this is the only work done on the terrestrial biodiversity status in the direct vicinity of a limited duration volcanic eruption. In this contribution, we document and assess the impact on the main plant and animal groups within the ecosystems during a volcanic eruption (Table 1). While some groups were clearly disadvantaged: ferns and herbaceous plants as well as invertebrates and saurians (lizards and geckos); other groups such as conifers and woody shrubs showed better resilience, as did the birds.This study is particularly important because of its location in a Mediterranean biodiversity hotspot13,14, harbouring a unique ecosystem of oceanic island organisms (38% of the Canary archipelago endemicity). Islands indeed exhibit a disproportionate amount of the world’s biodiversity but unfortunately a high number of extinctions have also occurred there14. The biodiversity in the south of the island is poorer than in the north. This is probably explained in part by the relatively frequent volcanic activity featuring seven major eruptions since 1585, including this one in 2021 (see15), which led to alternating destruction and neo-colonization processes.Concerning the flora, the Canary pine forest was the most affected ecosystem and vegetation type, as it is dominant in the vicinity of the new volcanic vents. The southern slopes of this forest were the most disturbed area due to the location of the volcano, combined with the prevailing northeasterly trade winds (Fig. 1). Tephra fallout and sulphurous gases were the main factors that affected the pine forest, over a vast surface area. Furthermore, the local xerophytic and thermophilous habitats also lost much of their surface area. In contrast to the pine forest, this drastic reduction was caused by the progressive downslope expansion of the lava flows.The Canary Island pine was thus notably affected by tephra fall, sulphuric acid aerosol12, and short episodes of acid rain. However, this conifer shows high resistance to temperature, confirming its great adaptation to volcanic events16, which is probably also one of the keys to its resistance to the more frequent present-day wildfires17. This pine species has evolved among volcanoes for the last 13 My16 and has adapted successfully to high temperatures. Moreover, thunderstorms with lightning occur in the Canaries together with abundant rainfall; consequently, wild forest fires should presumably not have been so frequent in the island’s past, before human colonization. In this habitat it is also remarkable that epiphytic lichens (U. articulata) apparently resisted on the pines until the 12th week, considering their high sensitivity to anthropogenic pollution18.The life cycle of flowering plants was drastically disrupted due to all the above factors, with great impact on foliage, photosynthesis, and growth. However, soil changes due to the deposition of tephra and its lixiviation by rain is one of the most dramatic factors affecting plants and a long-term impact of volcanic eruptions19. The nearest individuals to the crater were most directly affected by intense tephra falls and concentrated volcanic gases (SO2, HCl, HF, CO2). However, plants located in the nearest 200 m to the lava flows but at more than 2 km from the crater were presumably more disturbed by the high temperature of the slow-cooling lava and its lesser gas emissions.Large woody plants exhibited a better frequency of survival than smaller ones in the face of this extreme stress (Table S1 and19). In the Hekla area (Iceland), most trees have thickened trunks, indicating that those trees that survive have had a long life subjected to frequent volcanic damage19. Secondary woodiness of island plants (sensu20) has been traditionally related to drought20,21, ecological shift22 or a counter-selection of inbreeding depression in founding island populations23. However, this adaptation also favours the resistance of many shrubby plants to high temperatures close to craters and lava flows but primarily their resistance to the intense tephra falls that affect a much larger area. In addition, plant and stem height plays a fundamental role in overcoming the deep layers of deposits. This latter effect was particularly important up to 2.5 km from the crater (tephra thickness  > 30 cm) (Figs. 1 and 2), as the herbaceous plants were completely buried, sometimes to more than 1.5 m depth. Therefore, the seed bank has also probably been rendered largely non-functional. However, deposits were recorded over almost the whole island, indicating that longer lasting or more intense eruptions would severely affect an even larger area. Such events have been hitherto ignored in the intensely discussed “island woodiness” debate21,23,24,25,26,27. We found surviving populations of endemic woody taxa heavily impacted by tephra deposits close to lava flows, across a wide range of genera such as Rumex (R. lunaria), Echium (E. brevirame), Euphorbia (E. lamarckii, E. canariensis and E. balsamifera), Aeonium (A. davidbramwellii), Rubia (R. fruticosa), Schizogyne (S. sericea), Carlina (C. falcata) or Sonchus (S. hierrensis) (Table S2), which coincide with the general list of woody Canary plants20. Most members of these genera in other ecosystems on continents are mainly herbaceous. As such eruptions and their impacts due to ash depositions are frequent events on volcanic islands, e.g. several times within a century on La Palma, this is a “frequent” selective process at evolutionary time scales.With regard to the fauna, the invertebrate community collapsed during the first two weeks (Table S2), probably due to rapid deterioration of the growth state of plants. These changes in the invertebrates were caused by the tephra contacting the cuticular lipid layer28 and water loss due to tegument abrasion29. In this period, many insect pests (especially whitefly pupae) in banana plantations (farmers’ observations) were drastically reduced. This sudden decrease in insect populations affected the whole food web and probably caused part of the ecological collapse of saurian and some passerine communities30. In the case of lizards, smaller individuals seem to resist the adverse conditions better than large ones, as observed in other eruptions3. This could be linked to their lower food requirements and greater ease in finding refuges. Loss of body condition in lizards post-eruption has been recorded and negatively affects reproduction quality31. However, some lizards have shown a good ability to find food in the tephra substrate32. We found abundant tephra particles in some vertebrate droppings (lizards, birds, and mammals) during the eruption, probably involuntarily ingested. At least in bats, ingestion during feeding produces physiological stress that is likely related to baldness, high ectoparasite loads or possible mineral deficiencies33.As described in the Canary Islands, some passerines show high fidelity to their territories (see34). During the eruption, Sardinian warblers (Curruca melanocephala) maintained their territories until the imminent arrival of lava flows. Larger birds (kestrels F. tinnunculus, ravens C. corax and buzzards B. buteo) were well able to continue flying in the areas surrounding the crater. Furthermore, some cases like F. tinnunculus showed great feeding plasticity in the first couple of weeks. At least six times, kestrels tried to catch birds (especially small passerines and doves), contrary to their usual diet based on abundant lizards and insects35. Widening of trophic niches in island organisms has traditionally been interpreted as linked to disharmony in island ecosystems36,37,38. However, this plasticity is tremendously beneficial in ecological catastrophes, where food becomes exceptionally scarce. In the case of bats, their flight is limited by the delicate structure of their patagium, which can be damaged by the frequent pyroclastic tephra fall. Furthermore, scarcity of insects in the first few kilometres from the crater probably led to their displacement to other more distant and richer food resource zones.As we learned from the movement capacity of the vertebrate animals that still inhabited the affected area, those with greater mobility, birds and bats, resisted the eruptive process much better than those with less mobility, e.g. saurians.Lastly, during this destructive event on La Palma, we had the opportunity to increase our knowledge of how ecological-evolutionary adaptations have favoured the survival of insular organisms. Such responses are traditionally mentioned in the context of island biology. As already mentioned, one of the most interesting findings verifies the remarkable adaptation of Canary Island pine trees (P. canariensis) to volcanism (see16), including extremely harsh ecological conditions. Other insular trends related to the prevalence of woodiness in insular flowering plants20,21, or the high trophic plasticity of some vertebrates on oceanic islands36, have not previously been associated with their potential evolution along with volcanic processes. However, such evolutionary adaptations most likely played an important role in the survival of plants and animals affected by the volcano. For this reason, it is worth considering and debating whether these previously mentioned evolutionary processes are in fact also linked to repeated volcanic episodes on oceanic islands. More

Gardner TA, Cote IM, Gill JA, Grant A, Watkinson AR. Long-term region-wide declines in Caribbean corals. Science. 2003;301:958–60.CAS 
PubMed 

Google Scholar 
Knowlton N. The future of coral reefs. Proc Natl Acad Sci USA. 2001;98:5419–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Worm B, Barbier EB, Beaumont N, Duffy JE, Folke C, Halpern BS, et al. Impacts of biodiversity loss on ocean ecosystem services. Science. 2006;314:787–90.CAS 
PubMed 

Google Scholar 
Dudgeon SR, Aronson RB, Bruno JF, Precht WF. Phase shifts and stable states on coral reefs. Mar Ecol Prog Ser. 2010;413:201–16.
Google Scholar 
Bell JJ, Davy SK, Jones T, Taylor MW, Webster NS. Could some coral reefs become sponge reefs as our climate changes? Glob Climate Change. 2013;19:2613–24.
Google Scholar 
McMurray SE, Henkel TP, Pawlik JR. Demographics of increasing populations of the giant barrel sponge Xestospongia muta in the Florida Keys. Ecology. 2010;91:560–70.PubMed 

Google Scholar 
Bell JJ. The functional roles of marine sponges. Est Coast Shelf Sci. 2008;79:341–53.
Google Scholar 
Lesser MP, Slattery M. Will coral reef sponges be winners in the Anthropocene? Glob Change Biol. 2020;26:3202–11.
Google Scholar 
Pankey MS, Plachetzki DC, Macartney KJ, Gastaldi M, Slattery M, Gochfeld DJ, et al. Co-phylogeny and convergence shape holobiont evolution in sponge-microbe symbioses. Nat Ecol Evol. 2022;6:750–62.
Google Scholar 
Lesser MP, Slattery M, Mobley CD. Biodiversity and functional ecology of mesophotic coral reefs. Ann Rev Ecol Syst. 2018;49:49–71.
Google Scholar 
Diaz MC, Rützler K. Sponges: an essential component of Caribbean coral reefs. Bull Mar Sci. 2001;69:535–46.
Google Scholar 
Wulff JL. Ecological interactions and the distribution, abundance, and diversity of sponges. Adv Mar Biol. 2012;61:273–344.PubMed 

Google Scholar 
Lesser MP. Benthic-pelagic coupling on coral reefs: feeding and growth of Caribbean sponges. J Exp Mar Biol Ecol. 2006;328:277–88.
Google Scholar 
Perea-Blazquez A, Davy SK, Bell JJ. Estimates of particulate organic carbon flowing from the pelagic environment to the benthos through sponge assemblages. PLoS One. 2012;7:e29569.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lesser MP, Slattery M. Ecology of Caribbean sponges: are top-down or bottom-up processes more important? PLoS One. 2013;8:e79799.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pawlik JR. The chemical ecology of sponges on Caribbean reefs: natural products shape natural systems. BioScience. 2011;61:888–98.
Google Scholar 
Slattery M, Gochfeld DJ. Chemical interactions among marine competitors, and host-pathogens. In: Fattorusso, E, Gerwick, WH, Taglialatela-Scafati, O (eds). Handbook of Marine Natural Products. Springer, 2012. pp. 824–59.Thacker RW, Freeman CJ. Sponge-microbe symbioses: recent advances and new directions. Adv Mar Biol. 2012;62:57–112.PubMed 

Google Scholar 
Taylor MW, Radax R, Steger D, Wagner M. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol Biol Rev. 2007;71:295–347.CAS 

Google Scholar 
Schmitt S, Tsai P, Bell J, Fromont J, Ilan M, Lindquist N, et al. Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges. ISME J. 2012;6:564–76.CAS 
PubMed 

Google Scholar 
Gloeckner V, Wehrl M, Moitinho-Silva L, Gernert C, Schupp P, Pawlik JR, et al. The HMA-LMA dichotomy revisited: an electron microscopical survey of 56 sponge species. Biol Bull. 2014;227:78–88.PubMed 

Google Scholar 
Hentschel U, Fieseler L, Wehrl M, Gernert C, Steinert M, Hacker J, et al. Microbial diversity of marine sponges. Prog Mol Subcell Biol. 2003;37:59–88.CAS 
PubMed 

Google Scholar 
Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformation in marine symbioses. Trends Microbiol. 2010;18:455–63.CAS 
PubMed 

Google Scholar 
Zhang F, Jonas L, Lin H, Hill RT. Microbially mediated nutrient cycles in marine sponges. FEMS Microbiol Ecol. 2019;95:115.
Google Scholar 
Schläppy M-L, Schöttner SI, Lavik G, Kuypers MMM, de Beer D, Hoffmann F. Evidence of nitrification and denitrification in high and low microbial abundance sponges. Mar Biol. 2010;157:593–602.PubMed 

Google Scholar 
Giles EC, Kamke J, Moitinho-Silva L, Taylor MW, Hentschel U, Ravasi T, et al. Bacterial community profiles in low microbial abundance sponges. FEMS Microbiol Ecol. 2013;83:232–41.CAS 
PubMed 

Google Scholar 
Weisz JB, Lindquist N, Martens CS. Do associated microbial abundances impact marine demosponge pumping rates and tissue densities. Oecologia. 2008;155:367–76.PubMed 

Google Scholar 
de Goeij JM, van Oevelen D, Vermiej MJA, Osinga R, Middelburg JJ, de Goeij AFPM, et al. Surviving in a marine desert: the sponge loop retains resources within coral reefs. Science. 2013;342:108–10.PubMed 

Google Scholar 
de Goeij JM, Lesser MP, Pawlik JR. Nutrient fluxes and ecological functions of coral reef sponges in a changing ocean. In: Carballo, J, Bell, J eds. Climate Change, Ocean Acidification and Sponges. Springer, 2017. pp 373–410.Tanaka Y, Miyajima T, Wtanabe A, Nadaoka K, Yamamoto T, Ogawa H. Distribution of dissolved organic carbon and nitrogen in a coral reef. Coral Reefs. 2011;30:533–41.
Google Scholar 
Lesser MP, Slattery M, Laverick JH, Macartney KJ, Bridge TC. Global community breaks at 61 m on mesophotic coral reefs. Global Ecol Biogeogr. 2019;28:1403–16.
Google Scholar 
Lønborg C, Álvarez-Salgado XA, Duggan S, Carreira C. Organic matter bioavailability in tropical coastal waters: The Great Barrier Reef. Limnol Oceanogr. 2018;63:1015–35.
Google Scholar 
Macartney KJ, Abraham AC, Slattery M, Lesser MP. Growth and feeding in the sponge Agelas tubulata from shallow to mesophotic depths on Grand Cayman Island. Ecosphere. 2021;12:e03764.
Google Scholar 
Ribes M, Coma R, Atkinson MJ, Kinzie RA. Particle removal by coral reef communities: picoplankton is a major source of nitrogen. Mar Ecol Prog Ser. 2003;257:13–23.
Google Scholar 
Ribes M, Coma R, Atkinson MJ, Kinzie RA. Sponges and ascidians control removal of particulate organic nitrogen from coral reef water. Limnol Oceanogr. 2005;50:1480–9.CAS 

Google Scholar 
Maldonado M, Ribes M, van Duyl FC. Nutrient fluxes through sponges: biology, budgets, and ecological implications. Adv Mar Biol. 2012;62:113–82.PubMed 

Google Scholar 
Seutin G, White BN, Boag PT. Preservation of avian blood and tissue samples for DNA analyses. Can J Zool. 1991;69:82–90.CAS 

Google Scholar 
Abraham AC, Gochfeld DJ, Macartney K, Mellow A, Lesser MP, Slattery M. Biochemical variability in sponges across the Caribbean basin. Invertebr Biol. 2021;140:e12341.
Google Scholar 
Sunagawa S, Woodley CM, Medina M. Threatened corals provide underexplored microbial habitats. PLoS One. 2010;5:e9554.PubMed 
PubMed Central 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 

Google Scholar 
Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Microb Ecol. 2015;75:129–37.
Google Scholar 
Simion P, Phillippe H, Baurain D, Jager M, Richter RJ, Di Franco A, et al. A Large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr Biol. 2017;27:958–67.CAS 
PubMed 

Google Scholar 
Katoh K, Misawa K, Kuma KI, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR, et al. vegan: Community Ecology Package. R package version 2.5-5. https://CRAN.R-project.org/package=vegan. Released May, 2019.Pinheiro J, Bates D, DebRoy S, Sarkar D, EISPACK Authors, Heisterkamp S, et al. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-155. https://svn.r-project.org/R-packages/trunk/nlme/. Released Jan, 2022.Kindt R, Coe R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre, ICRAF, 2005. https://www.worldagroforestry.org/publication/tree-diversity-analysis-manual-and-software-common-statistical-methods-ecological-and.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
PubMed Central 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Westbrook A, Ramsdell J, Schuelke T, Normington L, Bergeron RD, Thomas WK, et al. PALADIN: protein alignment for functional profiling whole metagenome shotgun data. Bioinformatics. 2017;33:1473–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Robinson MD, McCarthy DG, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 

Google Scholar 
Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
PubMed 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Blin K, Shaw S, Kautsar SA, Medema MH, Weber T. The antiSMASH database version 3: increased taxonomic coverage and new query features for modular enzymes. Nucleic Acids Res. 2009;49:D639–43.
Google Scholar 
Conte-Jerpe IE, Thompson PD, Wong CWM, Oliveira NL, Duprey NN, Moynihan MA, et al. Trophic strategy and bleaching resistance in reef-building corals. Sci Adv. 2020;6:eaaz5443.
Google Scholar 
Jackson AL, Inger R, Parnell AC, Bearhop S. Comparing isotopic niche widths among and within communities: SIBER-Stable Isotope Bayesian Ellipses. Anim Ecol. 2011;80:595–602.
Google Scholar 
Thomas T, Moitinho-Silva L, Lurgi M, Björk JR, Easson C, Astudillo-Garcia C, et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Comm. 2016;7:11870.CAS 

Google Scholar 
Erwin PM, Coma R, López-Sendino P, Serrano E, Ribes M. Stable symbionts across the HMA-LMA dichotomy: low seasonal and inter-annual variation in sponge-associated bacteria from taxonomically diverse hosts. FEMS Microbiol Ecol. 2015;91:fiv115.PubMed 

Google Scholar 
Moitinho-Silva L, Steinert G, Nielsen S, Hardoim CCP, Wu Y-C, McCormack GP. Predicting the HMA-LMA status in marine sponges by machine learning. Front Microbiol. 2017;8:752.PubMed 
PubMed Central 

Google Scholar 
Campana S, Demey C, Busch K, Hentschel U, Muyzer G, de Goeij J. Marine sponges maintain stable bacterial communities between reef sites with different coral to algae cover ratios. FEMS Microbiol Ecol. 2021;97:fiab115.CAS 
PubMed 
PubMed Central 

Google Scholar 
Freeman CJ, Thacker RW. Complex interactions between marine sponges and their symbiotic microbial communities. Limnol Oceanogr. 2011;56:1577–86.
Google Scholar 
Siegel A, Kamke J, Hochmuth T, Piel J, Richter M, Liang C, et al. Single-cell genomic reveals the lifestyle of Poribacteria, a candidate phylum symbiotically associated with marine sponges. ISME J. 2011;5:61–70.
Google Scholar 
Bayer K, Jahn MT, Slaby BM, Moitinho-Silva L, Hentschel U. Marine sponges as Chloroflexi hot spots: genomic insights and high resolution visualization of an abundant and diverse symbiotic clade. mSystems. 2018;3:e00150–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Fan L, Reynolds D, Liu M, Thomas T. Functional equivalence and evolutionary convergence in complex communities of microbial sponge symbionts. Proc Natl Acad Sci USA. 2012;109:1878–87.
Google Scholar 
Ribes M, Jiménez E, Yahel G, López-Sendino P, Diez B, Massana R, et al. Functional convergence of microbes associated with temperate marine sponges. Environ Microbiol. 2012;14:1224–39.CAS 
PubMed 

Google Scholar 
Thomas T, Rusch D, DeMaere MZ, Yung PY, Lewis M, Halpern A, et al. Functional genomic signatures of sponge bacteria reveal unique and shared features of symbiosis. ISME J. 2010;4:1557–67.CAS 
PubMed 

Google Scholar 
Fiore CL, Labrie M, Jarett JK, Lesser MP. Transcriptional activity of the giant barrel sponge, Xestospongia muta holobiont: molecular evidence for metabolic interchange. Front Microbiol. 2015;6:364.PubMed 
PubMed Central 

Google Scholar 
Engel S, Pawlik JR. Allelopathic activities of sponge extracts. Mar Ecol Prog Ser. 2000;207:273–82.
Google Scholar 
Gochfeld DJ, Kamel HN, Olson JB, Thacker RW. Trade-offs in defensive metabolite production but not ecological function in healthy and diseased sponges. J Chem Ecol. 2012;38:451–62.CAS 
PubMed 

Google Scholar 
van Duyl FC, Mueller B, Meesters EH. Spatio-temporal variation in stable isotopic signatures (δ13C and δ15N) of sponges on the Saba Bank. PeerJ. 2018;6:e5460.PubMed 
PubMed Central 

Google Scholar 
Fiore CL, Baker DM, Lesser MP. Nitrogen biogeochemistry in the Caribbean sponge, Xestospongia muta: a source or sink of dissolved inorganic nitrogen? PLoS One. 2013;8:e72961.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hudspith M, de Goeij JM, Streekstra M, Kornder NA, Bougoure J, Guagliardo P, et al. Harnessing solar power: photoautotrophy supplements the diet of a low-light dwelling sponge. ISME J. 2022; https://doi.org/10.1038/s41396-022-01254-3.Shih JL, Selph KE, Wall CB, Wallsgrove NJ, Lesser MP, Popp BN. Trophic ecology of the tropical Pacific sponge Mycale gradis inferred from amino acid compound-specific isotopic analyses. Microb Ecol. 2020;79:495–510.CAS 
PubMed 

Google Scholar 
Macartney KJ, Slattery M, Lesser MP. Trophic ecology of Caribbean sponges in the mesophotic zone. Limnol Oceanogr. 2021;66:1113–24.CAS 

Google Scholar 
Southwell MW, Popp BN, Martens CS. Nitrification controls on fluxes and isotopic composition of nitrate from Florida Keys sponges. Mar Chem. 2008;108:96–108.CAS 

Google Scholar 
Lamb K, Swart PK. The carbon and nitrogen isotopic values of particulate organic material from the Florida Keys: a temporal and spatial study. Coral Reefs. 2008;27:351–62.
Google Scholar 
Ferrier-Pagès C, Leal MG. Stable isotopes as tracers of trophic interactions in marine mutualistic symbioses. Ecol Evol. 2019;9:723–40.PubMed 

Google Scholar 
McMurray SE, Stubler AD, Erwin PM, Finelli CM, Pawlik JR. A test of the sponge-loop hypothesis for emergent Caribbean reef sponges. Mar Ecol Prog Ser. 2018;588:1–14.CAS 

Google Scholar 
Freeman CJ, Easson CG, Baker DM. Metabolic diversity and niche structure in sponges from the Miskito Cays, Honduras. PeerJ. 2014;2:e695.PubMed 
PubMed Central 

Google Scholar 
Freeman CJ, Easson CG, Matterson KO, Thacker RW, Baker DM, Paul VJ. Microbial symbionts and ecological divergence of Caribbean sponges: a new perspective on an ancient association. ISME J. 2020;14:1571–83.PubMed 
PubMed Central 

Google Scholar 
Poppell E, Weisz J, Spicer L, Massaro A, Hill A, Hill M. Sponge heterotrophic capacity and bacterial community structure in high‐and low‐microbial abundance sponges. Mar Ecol. 2014;35:414–24.
Google Scholar 
Morganti TM, Ribes M, Yahel G, Coma R. Size is the major determinant of pumping rates in marine sponges. Front Physiol. 2019;10:1474.PubMed 
PubMed Central 

Google Scholar 
Rix L, Ribes M, Coma R, Jahn MT, de Goeij JM, van Oevelen D, et al. Heterotrophy in the earliest gut: a single-cell view of heterotrophic carbon and nitrogen assimilation in sponge-microbe symbioses. ISME J. 2020;14:2554–67.CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Brien PA, Tan S, Yang C, Frade PR, Andreakis N, Smith HA, et al. Diverse coral reef invertebrates exhibit patterns of phylosymbiosis. ISME J. 2020;14:2211–22.PubMed 
PubMed Central 

Google Scholar 
Erwin PM, Thacker RW. Incidence and identity of photosynthetic symbionts in Caribbean coral reef sponge assemblages. J Mar Biol Assoc UK. 2007;87:1683–92.CAS 

Google Scholar 
Palumbi SR. Tactics of acclimation: morphological changes of sponges in an unpredictable environment. Science. 1984;225:1478–80.CAS 
PubMed 

Google Scholar 
Slattery M, Gochfeld DJ, Diaz MC, Thacker RW, Lesser MP. Variability in chemical defense across a shallow to mesophotic depth gradient in the Caribbean sponge Plakortis angulospiculatus. Coral Reefs. 2016;35:11–22.
Google Scholar 
Morganti T, Coma R, Yahel G, Ribes M. Trophic niche separation that facilitates co‐existence of high and low microbial abundance sponges is revealed by in situ study of carbon and nitrogen fluxes. Limnol Oceanogr. 2017;62:1963–83.CAS 

Google Scholar 
Maldonado M. Sponge waste that fuels marine oligotrophic food webs: a re-assessment of its origin and nature. Mar Ecol. 2016;37:477–91.
Google Scholar  More

Williams, R. et al. Prioritizing global marine mammal habitats using density maps in place of range maps. Ecography 37, 212–220 (2014).
Google Scholar 
Tyack, P. L. & Clark, C. W. Communication and acoustic behavior of dolphins and whales in Hearing by whales and dolphins 156–224 (Springer, 2000).Davis, G. E. et al. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Glob. Change Biol. 26, 4812 (2020).ADS 

Google Scholar 
Lomac-MacNair, K. S. et al. Marine mammal visual and acoustic surveys near the Alaskan Colville River Delta. Polar Biol. 42, 441–448 (2018).
Google Scholar 
Keen, E., Hendricks, B., Wray, J., Alidina, H. & Picard, C. Integrating passive acoustic and visual surveys for marine mammals in coastal habitats in 176th Meeting of Acoustical Society of America. 1 edn.Gregr, E. J., Baumgartner, M. F., Laidre, K. L. & Palacios, D. M. Marine mammal habitat models come of age: The emergence of ecological and management relevance. Endang. Species Res. 22, 205–212 (2013).
Google Scholar 
Hastie, G. D., Wilson, B., Wilson, L., Parsons, K. M. & Thompson, P. M. Functional mechanisms underlying cetacean distribution patterns: Hotspots for bottlenose dolphins are linked to foraging. Mar. Biol. 144, 397–403 (2004).
Google Scholar 
Lambert, C., Mannocci, L., Lehodey, P. & Ridoux, V. Predicting cetacean habitats from their energetic needs and the distribution of their prey in two contrasted tropical regions. PLoS ONE 9, e105958 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Huot, Y. et al. Does chlorophyll a provide the best index of phytoplankton biomass for primary productivity studies?. Biogeosci. Discuss. 4, 707–745 (2007).ADS 

Google Scholar 
Etnoyer, P. et al. Sea-surface temperature gradients across blue whale and sea turtle foraging trajectories off the Baja California Peninsula, Mexico. Deep Sea Res. II 53, 340–358 (2006).ADS 

Google Scholar 
Shabangu, F. W. et al. Seasonal occurrence and diel calling behaviour of Antarctic blue whales and fin whales in relation to environmental conditions off the west coast of South Africa. J. Mar. Syst. 190, 25–39 (2019).
Google Scholar 
Haida Nation & Parks Canada Agency. Gwaii Haanas Gina ’Waadluxan Kilguhlga. Land-Sea-People Management Plan. 33 (© Council of the Haida Nation and Her Majesty the Queen in Right of Canada, represented by the Chief Executive Officer of Parks Canada, 2018).Ford, J. K. B. Marine Mammals of British Columbia. (Royal BC Museum, 2014).Allen, A. S., Yurk, H., Vagle, S., Pilkington, J. & Canessa, R. The underwater acoustic environment at SGaan Kinghlas-Bowie Seamount Marine Protected Area: Characterizing vessel traffic and associated noise using satellite AIS and acoustic datasets. Mar. Pollut. Bull. 128, 82–88 (2018).CAS 
PubMed 

Google Scholar 
Ainslie, M. A. Principles of Sonar Performance Modeling. (Springer, 2010).Collins, M. D. A split-step Padé solution for the parabolic equation method. J. Acoust. Soc. Am. 93, 1736–1742 (1993).ADS 

Google Scholar 
Porter, M. B. & Bucker, H. P. Gaussian beam tracing for computing ocean acoustic fields. J. Acoust. Soc. Am. 82, 1349–1359 (1987).ADS 

Google Scholar 
Mouy, X., MacGillivray, A. O., Vallarta, J. H., Martin, B. & Delarue, J. J.-Y. Ambient Noise and Killer Whale Monitoring near Port Metro Vancouver’s Proposed Terminal 2 Expansion Site: July–September 2012. (Technical report by JASCO Applied Sciences for Hemmera, 2012).Ford, J. et al. Distribution and relative abundance of cetaceans in western Canadian waters from ship surveys, 2002–2008. Can. Tech. Rep. Fish. Aquat. Sci. 2913, 51 (2010).
Google Scholar 
Wright, B. M., Nichol, L. M. & Doniol-Valcroze, T. Spatial density models of cetaceans in the Canadian Pacific estimated from 2018 ship-based surveys. DFO Can. Sci. Advis. Sec. Res. Doc. 2021, 49 (2021).
Google Scholar 
Devred, E., Hardy, M. & Hannah, C. Satellite observations of the Northeast Pacific Ocean. Can. Tech. Rep. Hydrogr. Ocean Sci. 335, 46 (2021).
Google Scholar 
Saha, K. et al. NOAA National centers for environmental information. Dataset https://doi.org/10.7289/v52j68xx (2018).Article 

Google Scholar 
NASA Goddard Space Flight Center, Ocean Ecology Laboratory & Ocean Biology Processing Group. (NASA OB.DAAC, Greenbelt, MD, USA. https://doi.org/10.5067/AQUA/MODIS/L3B/CHL/2018. Accessed 3 Feb 2021.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B Stat. Methodol. 73, 3–36 (2011).MathSciNet 
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/ (2021).Ogle, D. H., Wheeler, P. & Dinno, A. FSA: Fisheries Stock Analysis. R package version 0.8.32. https://github.com/droglenc/FSA (2021).Payne, R. S. & McVay, S. Songs of humpback whales. Science 173, 585–597 (1971).ADS 
CAS 
PubMed 

Google Scholar 
Rekdahl, M. L. et al. Non-song social call bouts of migrating humpback whales. J. Acoust. Soc. Am. 137, 3042–3053 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Oswald, J. N., Rankin, S. & Barlow, J. To whistle or not to whistle? Geographic variation in the whistling behavior of small odontocetes. Aquat. Mamm. 34, 288–302 (2008).
Google Scholar 
Rankin, S., Oswald, J., Barlow, J. P. & Lammers, M. Patterned burst-pulse vocalizations of the northern right whale dolphin, Lissodelphis borealis. J. Acoust. Soc. Am. 121, 1213–1218. https://doi.org/10.1121/1.2404919 (2007).Article 
ADS 
PubMed 

Google Scholar 
Arranz, P. et al. Discrimination of fast click-series produced by tagged Risso’s dolphins (Grampus griseus) for echolocation or communication. J. Exp. Biol. 219, 2898–2907. https://doi.org/10.1242/jeb.144295 (2016).Article 
CAS 
PubMed 

Google Scholar 
Halpin, L. R., Towers, J. R. & Ford, J. K. First record of common bottlenose dolphin (Tursiops truncatus) in Canadian Pacific waters. Mar. Biodivers. Rec. 11, 1–5 (2018).
Google Scholar 
Nikolich, K. & Towers, J. R. Vocalizations of common minke whales (Balaenoptera acutorostrata) in an eastern North Pacific feeding ground. Bioacoustics 29, 97–108 (2020).
Google Scholar 
Money, J. H. & Trites, A. W. A preliminary assessment of the status of marine mammal populations and associated research needs for the west coast of Canada. Report No. Final Report, 80 (Fisheries and Oceans Canada, 1998).Gregr, E. J. & Trites, A. W. Predictions of critical habitat for five whale species in the waters of coastal British Columbia. Can. J. Fish. Aquat. Sci. 58, 1265–1285 (2001).
Google Scholar 
Ou, H., Au, W. W. L., Van Parijs, S., Oleson, E. M. & Rankin, S. Discrimination of frequency-modulated Baleen whale downsweep calls with overlapping frequencies. J. Acoust. Soc. Am. 137, 3024–3032. https://doi.org/10.1121/1.4919304 (2015).Article 
ADS 
PubMed 

Google Scholar 
Mellinger, D. K., Stafford, K. M., Moore, S. E., Dziak, R. P. & Matsumoto, H. An overview of fixed passive acoustic observation methods for cetaceans. Oceanography 20, 36–45 (2007).
Google Scholar 
Stafford, K. M., Citta, J. J., Moore, S. E., Daher, M. A. & George, J. E. Environmental correlates of blue and fin whale call detections in the North Pacific Ocean from 1997 to 2002. Mar. Ecol. Prog. Ser. 395, 37–53 (2009).ADS 

Google Scholar 
Burnham, R., Duffus, D. & Mouy, X. The presence of large whale species in Clayoquot Sound and its offshore waters. Cont. Shelf Res. 177, 15–23 (2019).ADS 

Google Scholar 
Burtenshaw, J. C. et al. Acoustic and satellite remote sensing of blue whale seasonality and habitat in the Northeast Pacific. Deep Sea Res. II 51, 967–986 (2004).ADS 

Google Scholar 
Calambokidis, J., Barlow, J., Ford, J. K. B., Chandler, T. E. & Douglas, A. B. Insights into the population structure of blue whales in the Eastern North Pacific from recent sightings and photographic identification. Mar. Mamm. Sci. 25, 816–832 (2009).
Google Scholar 
Jackson, J. M., Thomson, R. E., Brown, L. N., Willis, P. G. & Borstad, G. A. Satellite chlorophyll off the British Columbia Coast, 1997–2010. J. Geophys. Res. Oceans 120, 4709–4728 (2015).ADS 

Google Scholar 
Evans, R., English, P. A., Anderson, S. C., Gauthier, S. & Robinson, C. L. Factors affecting the seasonal distribution and biomass of E. pacifica and T. spinifera along the Pacific coast of Canada: A spatiotemporal modelling approach. PLoS ONE 16, e0249818 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore, S. E., Watkins, W. A., Daher, M. A., Davies, J. R. & Dahlheim, M. E. Blue whale habitat associations in the Northwest Pacific: Analysis of remotely-sensed data using a Geographic Information System. Oceanography 15, 1–10 (2002).
Google Scholar 
Lockyer, C. Review of Baleen Whale (Mysticeti) reproduction and implications for management. Rep. Int. Whal. Commn Spec. Issue 6, 27–50 (1984).
Google Scholar 
Ohsumi, S. M. N. Growth of fin whale in the Northern Pacific Ocean. Sci. Rep. Whale Res. Inst. 13, 97–133 (1958).
Google Scholar 
Watkins, W. A. et al. Seasonality and distribution of whale calls in the North Pacific. Oceanography 13, 62–67 (2000).
Google Scholar 
Watkins, W. A., Tyack, P., Moore, K. E. & Bird, J. E. The 20-Hz signals of finback whales (Balaenoptera physalus). J. Acoust. Soc. Am. 82, 1901–1912 (1987).ADS 
CAS 
PubMed 

Google Scholar 
Stafford, K. M., Mellinger, D. K., Moore, S. E. & Fox, C. G. Seasonal variability and detection range modeling of baleen whale calls in the Gulf of Alaska, 1999–2002. J. Acoust. Soc. Am. 122, 3378–3390 (2007).ADS 
PubMed 

Google Scholar 
Koot, B. Winter Behaviour and Population Structure of Fin Whales (Balaenoptera physalus) in British Columbia inferred from passive acoustic data (University of British Columbia, 2015).
Google Scholar 
Pilkington, J. F., Stredulinsky, E. H., Abernethy, R. M. & Ford, J. K. B. Patterns of Fin whale (Balaenoptera physalus) Seasonality and Relative Distribution in Canadian Pacific Waters Inferred from Passive Acoustic Monitoring. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Best, B. D., Fox, C. H., Williams, R., Halpin, P. H. & Paquet, P. C. Updated Marine Mammal Distribution and Abundance Estimates in British Columbia (Springer, 2015).
Google Scholar 
Clarke, C. & Jamieson, G. Identification of ecologically and biologically significant areas in the Pacific North Coast integrated management area: Phase II: Final report. Can. Tech. Rep. Fish. Aquat. Sci. 2678, 59 (2006).
Google Scholar 
Nichol, L. M. et al. Distribution, movements and habitat fidelity patterns of Fin Whales (Balaenoptera physalus) in Canadian Pacific Waters. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Nichol, L. M. & Ford, J. K. B. Information in Support of the Identification of Habitat of Special Importance to Fin Whales (Balaenoptera physalus) in Canadian Pacific Waters. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Mizroch, S. A., Rice, D. W., Zwiefelhofer, D., Waite, J. & Perryman, W. L. Distribution and movements of fin whales in the North Pacific Ocean. Mammal Rev. 39, 193–227 (2009).
Google Scholar 
Širović, A., Williams, L. N., Kerosky, S. M., Wiggins, S. M. & Hildebrand, J. A. Temporal separation of two fin whale call types across the eastern North Pacific. Mar. Biol. 160, 47–57 (2013).PubMed 

Google Scholar 
Flinn, R. D., Trites, A. W., Gregr, E. J. & Perry, R. I. Diets of fin, sei, and sperm whales in British Columbia: an analysis of commercial whaling records, 1963–1967. Mar. Mamm. Sci. 18, 663–679 (2002).
Google Scholar 
Barnes, R. S. K. & Hughes, R. N. An Introduction to Marine Ecology (Wiley, 1999).
Google Scholar 
Romagosa, M. et al. Food talks: 40-hz fin whale calls are associated with prey biomass. Proc. R. Soc. B 288, 20211156 (2021).PubMed 
PubMed Central 

Google Scholar 
Gregr, E. J., Nichol, L., Ford, J. K., Ellis, G. & Trites, A. W. Migration and population structure of northeastern Pacific whales off coastal British Columbia: An analysis of commercial whaling records from 1908–1967. Mar. Mamm. Sci. 16, 699–727 (2000).
Google Scholar 
Williams, R. & Thomas, L. Distribution and abundance of marine mammals in the coastal waters of British Columbia, Canada. J. Cetac. Res. Manage. 9, 15 (2007).
Google Scholar 
Dalla Rosa, L., Ford, J. K. & Trites, A. W. Distribution and relative abundance of humpback whales in relation to environmental variables in coastal British Columbia and adjacent waters. Contin. Shelf Res. 36, 89–104 (2012).ADS 

Google Scholar 
Winn, H. E. & Winn, L. K. The song of the humpback whale Megaptera novaeangliae in the West Indies. Mar. Biol. 47, 97–114. https://doi.org/10.1007/BF00395631 (1978).Article 

Google Scholar 
Baker, C. S. et al. Population characteristics and migration of summer and late-season humpback whales (Megaptera novaeangliae) in southeastern Alaska. Mar. Mamm. Sci. 1, 304–323 (1985).ADS 

Google Scholar 
McSweeney, D., Chu, K., Dolphin, W. & Guinee, L. North Pacific humpback whale songs: A comparison of southeast Alaskan feeding ground songs with Hawaiian wintering ground songs. Mar. Mamm. Sci. 5, 139–148 (1989).
Google Scholar 
Norris, T. F., McDonald, M. & Barlow, J. Acoustic detections of singing humpback whales (Megaptera novaeangliae) in the eastern North Pacific during their northbound migration. J. Acoust. Soc. Am. 106, 506–514 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Clark, C. W. & Clapham, P. J. Acoustic monitoring on a humpback whale (Megaptera novaeangliae) feeding ground shows continual singing into late spring. Proc. R. Soc. Lond. B 271, 1051–1057 (2004).
Google Scholar 
Stimpert, A. K., Peavey, L. E., Friedlaender, A. S. & Nowacek, D. P. Humpback whale song and foraging behavior on an Antarctic feeding ground. PLoS ONE 7, e51214 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kowarski, K., Evers, C., Moors-Murphy, H., Martin, B. & Denes, S. L. Singing through winter nights: Seasonal and diel occurrence of humpback whale (Megaptera novaeangliae) calls in and around the Gully MPA, offshore eastern Canada. Mar. Mamm. Sci. 34, 169–189 (2018).
Google Scholar 
Nichol, L. M., Abernethy, R., Flostrand, L., Lee, T. S. & Ford, J. K. B. Information relevant for the identification of critical habitats of north pacific humpback whales (Megaptera novaeangliae) in British Columbia. DFO Can. Sci. Advis. Sec. Res. Doc. (2010).Williams, R., Erbe, C., Ashe, E. & Clark, C. W. Quiet (er) marine protected areas. Mar. Pollut. Bull. 100, 154–161 (2015).CAS 
PubMed 

Google Scholar 
Gaston, A. J., Pilgrim, N. G. & Pattison, V. Humpback Whale (Megaptera novaeangliae) observations in Laskeek Bay, western Hecate Strait, in spring and early summer, 1990–2018. Can. Field Nat. 133, 263–269 (2019).
Google Scholar 
Robinson, C. L., Gower, J. F. & Borstad, G. Twenty years of satellite observations describing phytoplankton blooms in seas adjacent to Gwaii Haanas National Park Reserve, Canada. Can. J. Remote Sens. 30, 36–43 (2004).ADS 

Google Scholar 
Swartz, S. L., Taylor, B. L. & Rugh, D. J. Gray whale Eschrichtius robustus population and stock identity. Mamm. Rev. 36, 66–84 (2006).
Google Scholar 
Gaston, A. J. & Heise, K. Results of cetacean observations in Laskeek Bay, 1990–2003. Laskeek Bay Res. 55, 1–10 (2004).
Google Scholar 
Ford, J. K. et al. New insights into the northward migration route of gray whales between Vancouver Island, British Columbia, and southeastern Alaska. Mar. Mamm. Sci. 29, 325–337 (2013).
Google Scholar 
Burnham, R. E. & Duffus, D. A. The use of passive acoustic monitoring as a census tool of gray whale (Eschrichtius robustus) migration. Ocean Coast. Manag. 188, 105070 (2020).
Google Scholar 
Best, P. B. Social organization in sperm whales. In Physeter macrocephalus in Behavior of Marine Animals (eds Winn, H. E. & Olla, B. L.) 227–289 (Springer, 1979).
Google Scholar 
Jaquet, N. & Gendron, D. Distribution and relative abundance of sperm whales in relation to key environmental features, squid landings and the distribution of other cetacean species in the Gulf of California, Mexico. Mar. Biol. 141, 591–601 (2002).
Google Scholar 
Rice, D. W. Sperm whale Physeter macrocephalus Linnaeus, 1758. Handb. Mar. Mamm. 4, 177–233 (1989).
Google Scholar 
Whitehead, H. & Arnbom, T. Social organization of sperm whales off the Galapagos Islands, February–April 1985. Can. J. Zool. 65, 913–919 (1987).
Google Scholar 
Whitehead, H. Sperm whale: Physeter macrocephalus. In Encyclopedia of Marine Mammals 3rd edn (eds Würsig, B. et al.) 919–925 (Academic Press, 2018).
Google Scholar 
Mizroch, S. A. & Rice, D. W. Ocean nomads: Distribution and movements of sperm whales in the North Pacific shown by whaling data and Discovery marks. Mar. Mamm. Sci. 29, E136–E165 (2013).
Google Scholar 
Diogou, N. et al. Sperm whale (Physeter macrocephalus) acoustic ecology at Ocean Station PAPA in the Gulf of Alaska-Part 2: Oceanographic drivers of interannual variability. Deep Sea Res. I 150, 103044 (2019).
Google Scholar 
Ford, J. K. & Ellis, G. M. You are what you eat: Foraging specializations and their influence on the social organization and behavior of killer whales. in Primates and Cetaceans 75–98 (Springer, 2014).Ford, J. K. B. et al. Habitats of special importance to resident killer whales (Orcinus orca) off the West Coast of Canada. DFO Can. Sci. Advis. Sec. Res. Doc. (2017).Ford, J. K. B., Stredulinsky, E. H., Ellis, G. M., Durban, J. W. & Pilkington, J. F. Offshore Killer whales in Canadian pacific waters: Distribution, seasonality, foraging ecology, population status and potential for recovery. DFO Can. Sci. Advis. Sec. Res. Doc. (2014).Nichol, L. M. & Shackleton, D. M. Seasonal movements and foraging behaviour of northern resident killer whales (Orcinus orca) in relation to the inshore distribution of salmon (Oncorhynchus spp.) in British Columbia. Can. J. Zool. 74, 983–991 (1996).
Google Scholar 
Olesiuk, P. F., Ellis, G. M. & Ford, J. K. Life History and Population Dynamics of Northern Resident Killer Whales (Orcinus orca) in British Columbia (Canadian Science Advisory Secretariat Ottawa, 2005).
Google Scholar 
Newman, K. & Springer, A. Nocturnal activity by mammal-eating killer whales at a predation hot spot in the Bering Sea. Mar. Mamm. Sci. 24, 990 (2008).
Google Scholar 
Ford, J. K. B. et al. Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters. Can. J. Zool. 76, 1456–1471 (1998).
Google Scholar 
Barrett-Lennard, L. G., Ford, J. K. B. & Heise, K. A. The mixed blessing of echolocation: Differences in sonar use by fish-eating and mammal-eating killer whales. Anim. Behav. 51, 553–565 (1996).
Google Scholar 
Deecke, V. B., Ford, J. K. B. & Slater, P. J. B. The vocal behaviour of mammal-eating killer whales: Communicating with costly calls. Anim. Behav. 69, 395–405 (2005).
Google Scholar 
Ford, J. K. B. Call traditions and vocal dialects of killer whales (Orcinus orca) in British Columbia Ph.D. thesis, University of British Columbia (1984).Baird, R. W. Status of killer whales, Orcinus orca, Canada. Can. Field. Nat. 115, 676–701 (2001).
Google Scholar 
Ford, J. K. B., Stredulinsky, E. H., Towers, J. R. & Ellis, G. M. Information in Support of the Identification of Critical Habitat for Transient Killer Whales (Orcinus orca) off the West Coast of Canada. DFO Can. Sci. Advis. Sec. Res. Doc. (2013).Tyack, P. L., Johnson, M., Soto, N. A., Sturlese, A. & Madsen, P. T. Extreme diving of beaked whales. J. Exp. Biol. 209, 4238–4253 (2006).PubMed 

Google Scholar 
Baumann-Pickering, S. et al. Species-specific beaked whale echolocation signals. J. Acoust. Soc. Am. 134, 2293–2301 (2013).ADS 
PubMed 

Google Scholar 
Pike, G. C. Two records of Berardius bairdi from the coast of British Columbia. J. Mammal. 34, 98–104 (1953).
Google Scholar 
Pike, G. C. & MacAskie, I. Marine mammals of British Columbia. Fish. Res. Board Can. Bull. 171, 1–10 (1969).
Google Scholar 
Willis, P. M. & Baird, R. W. Sightings and strandings of beaked whales on the west coast of. Aquat. Mamm. 24, 21–25 (1998).
Google Scholar 
Jefferson, T. A. Phocoenoides dalli. Mamm. Spec. https://doi.org/10.2307/3504170 (1988).Article 

Google Scholar 
Boyd, C. et al. Estimation of population size and trends for highly mobile species with dynamic spatial distributions. Divers. Distrib. 24, 1–12 (2018).
Google Scholar 
Carretta, J. V., Taylor, B. L. & Chivers, S. J. Abundance and depth distribution of harbor porpoise (Phocoena phocoena) in northern California determined from a 1995 ship survey. Fish. Bull. 99, 29–29 (2001).
Google Scholar 
Willis, P. M. & Baird, R. W. Status of the dwarf sperm whale, Kogia simus, with special reference to Canada. Can. Field Nat. 112, 114–125 (1998).
Google Scholar 
Kyhn, L. A. et al. Clicking in a killer whale habitat: Narrow-band, high-frequency biosonar cliks of harbour porpoise (Phocoena phocoena) and Dall’s porpoise (Phocoenoides dalli). PLoS ONE 8, e63763 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Madsen, P., Carder, D., Bedholm, K. & Ridgway, S. Porpoise clicks from a sperm whale nose—Convergent evolution of 130 kHz pulses in toothed whale sonars?. Bioacoustics 15, 195–206 (2005).
Google Scholar 
Merkens, K. et al. Clicks of dwarf sperm whales (Kogia sima). Mar. Mamm. Sci. 34, 963–978 (2018).
Google Scholar 
Griffiths, E. T. et al. Detection and classification of narrow-band high frequency echolocation clicks from drifting recorders. J. Acoust. Soc. Am. 147, 3511–3522 (2020).ADS 
PubMed 

Google Scholar 
Baird, R. W. & Stacey, P. J. Status of Risso’s Dolphin, Grampus griseus, in Canada. Naturalist 5, 233142 (1991).
Google Scholar 
Benoit-Bird, K. J. & Au, W. W. Prey dynamics affect foraging by a pelagic predator (Stenella longirostris) over a range of spatial and temporal scales. Behav. Ecol. Sociobiol. 53, 364–373 (2003).
Google Scholar 
Benoit-Bird, K. J., Würsig, B. & Mfadden, C. J. Dusky dolphin (Lagenorhynchus obscurus) foraging in two different habitats: active acoustic detection of dolphins and their prey. Mar. Mamm. Sci. 20, 215–231 (2004).
Google Scholar 
Soldevilla, M. S., Wiggins, S. M. & Hildebrand, J. A. Spatial and temporal patterns of Risso’s dolphin echolocation in the Southern California Bight. J. Acoust. Soc. Am. 127, 124–132 (2010).ADS 
PubMed 

Google Scholar 
Soldevilla, M. S., Wiggins, S. M. & Hildebrand, J. A. Spatio-temporal comparison of Pacific white-sided dolphin echolocation click types. Aquat. Biol. 9, 49–62 (2010).
Google Scholar 
Taylor, F. The relationship of midwater trawl catches to sound scattering layers off the coast of northern British Columbia. J. Fish. Board Can. 25, 457–472 (1968).
Google Scholar 
Curtis, K. R., Howe, B. M. & Mercer, J. A. Low-frequency ambient sound in the North Pacific: Long time series observations. J. Acoust. Soc. Am. 106, 3189–3200 (1999).ADS 

Google Scholar 
Aroyan, J. L. et al. Acoustic models of sound production and propagation in Hearing by whales and dolphins 409–469 (Springer, 2000).
Google Scholar 
Cummings, W. C. & Thompson, P. O. Underwater sounds from the blue whale, Balaenoptera musculus. J. Acoust. Soc. Am. 50, 1193–1198 (1971).ADS 

Google Scholar 
McDonald, M. A., Calambokidis, J., Teranishi, A. M. & Hildebrand, J. A. The acoustic calls of blue whales off California with gender data. J. Acoust. Soc. Am. 109, 1728–1735 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Weirathmueller, M. J., Wilcock, W. S. D. & Soule, D. C. Source levels of fin whale 20 Hz pulses measured in the Northeast Pacific Ocean. J. Acoust. Soc. Am. 133, 741–749 (2013).ADS 
PubMed 

Google Scholar 
Vihtakari, M. ggOceanMaps: Plot Data on Oceanographic Maps using ‘ggplot2’. R package version 1.2.14. https://mikkovihtakari.github.io/ggOceanMaps/ (2022). More

Delavaux, C. S., Smith‐Ramesh, L. M. & Kuebbing, S. E. Beyond nutrients: a meta‐analysis of the diverse effects of arbuscular mycorrhizal fungi on plants and soils. Ecology 98, 2111–2119 (2017).Lugtenberg, B. & Kamilova, F. Plant-growth-promoting rhizobacteria. Annu. Rev. Microbiol. 63, 541–556 (2009).Article 
CAS 
PubMed 

Google Scholar 
Franche, C., Lindström, K. & Elmerich, C. Nitrogen-fixing bacteria associated with leguminous and non-leguminous plants. Plant Soil 321, 35–59 (2009).Article 
CAS 

Google Scholar 
Razanajatovo, M. et al. Autofertility and self‐compatibility moderately benefit island colonization of plants. Glob. Ecol. Biogeogr. 28, 341–352 (2019).Article 

Google Scholar 
Schrader, J., Wright, I. J., Kreft, H. & Westoby, M. A roadmap to plant functional island biogeography. Biol. Rev. (2021).Herridge, D. F., Peoples, M. B. & Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. Plant Soil 311, 1–18 (2008).Article 
CAS 

Google Scholar 
Vitousek, P. Nutrient cycling and limitation: Hawai’i as a model ecosystem. (Princeton Univ. Press, Princeton, NJ, 2004). Nutrient cycling and limitation: Hawai’i as a model ecosystem. Princeton Univ. Press, Princeton, NJ.Book 

Google Scholar 
Becking, L. G. M. B. Geobiologie of inleiding tot de milieukunde. (WP Van Stockum & Zoon, 1934).Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant–fungal interactions. N. Phytol. 204, 180–191 (2014).Article 

Google Scholar 
Delavaux, C. S. et al. Mycorrhizal fungi influence global plant biogeography. Nat. Ecol. Evol. 3, 424 (2019).Article 
PubMed 

Google Scholar 
Duchicela, J., Bever, J. D. & Schultz, P. A. Symbionts as Filters of Plant Colonization of Islands: Tests of Expected Patterns and Environmental Consequences in the Galapagos. Plants 9, 74 (2020).Article 
CAS 
PubMed Central 

Google Scholar 
Delavaux, C. S. et al. Mycorrhizal types influence island biogeography of plants. Commun. Biol. 4, 1–8 (2021).Article 

Google Scholar 
Simonsen, A. K., Dinnage, R., Barrett, L. G., Prober, S. M. & Thrall, P. H. Symbiosis limits establishment of legumes outside their native range at a global scale. Nat. Commun. 8, 1–9 (2017).Article 

Google Scholar 
Poole, P., Ramachandran, V. & Terpolilli, J. Rhizobia: from saprophytes to endosymbionts. Nat. Rev. Microbiol. 16, 291–303 (2018).Article 
CAS 
PubMed 

Google Scholar 
Sprent, J. I., Ardley, J. & James, E. K. Biogeography of nodulated legumes and their nitrogen‐fixing symbionts. N. Phytol. 215, 40–56 (2017).Article 
CAS 

Google Scholar 
Menge, D. N. Hedin, L. O. & Pacala, S. W. Nitrogen and phosphorus limitation over long-term ecosystem development in terrestrial ecosystems. (2012).Lambers, H., Raven, J. A., Shaver, G. R. & Smith, S. E. Plant nutrient-acquisition strategies change with soil age. Trends Ecol. evolution 23, 95–103 (2008).Article 

Google Scholar 
Walker, T. & Syers, J. K. The fate of phosphorus during pedogenesis. Geoderma 15, 1–19 (1976).Article 
CAS 

Google Scholar 
Jin, L. et al. Synergistic interactions of arbuscular mycorrhizal fungi and rhizobia promoted the growth of Lathyrus sativus under sulphate salt stress. Symbiosis 50, 157–164 (2010).Article 
CAS 

Google Scholar 
Afkhami, M. E. & Stinchcombe, J. R. Multiple mutualist effects on genomewide expression in the tripartite association between Medicago truncatula, nitrogen‐fixing bacteria and mycorrhizal fungi. Mol. Ecol. 25, 4946–4962 (2016).Article 
CAS 
PubMed 

Google Scholar 
Larimer, A. L., Clay, K. & Bever, J. D. Synergism and context dependency of interactions between arbuscular mycorrhizal fungi and rhizobia with a prairie legume. Ecology 95, 1045–1054 (2014).Article 
PubMed 

Google Scholar 
Primieri, S., Magnoli, S. M., Koffel, T. S., Stürmer, S. L. & Bever, J. D. Perennial, but not annual legumes synergistically benefit from infection with arbuscular mycorrhizal fungi and rhizobia: a meta‐analysis. N. Phytol. 233, 505-514 (2021).Larimer, A. L., Bever, J. D. & Clay, K. The interactive effects of plant microbial symbionts: a review and meta-analysis. Symbiosis 51, 139–148 (2010).Article 

Google Scholar 
Werner, G. D., Cornwell, W. K., Sprent, J. I., Kattge, J. & Kiers, E. T. A single evolutionary innovation drives the deep evolution of symbiotic N 2-fixation in angiosperms. Nat. Commun. 5, 1–9 (2014).Article 

Google Scholar 
Weigelt, P., König, C. & Kreft, H. GIFT- A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

Google Scholar 
Werner, G. D. et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc. Natl Acad. Sci. 115, 5229–5234 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bamba, M. et al. Wide distribution range of rhizobial symbionts associated with pantropical sea-dispersed legumes. Antonie van. Leeuwenhoek 109, 1605–1614 (2016).Article 
PubMed 

Google Scholar 
Chen, W.-M., Lee, T.-M., Lan, C.-C. & Cheng, C.-P. Characterization of halotolerant rhizobia isolated from root nodules of Canavalia rosea from seaside areas. FEMS Microbiol. Ecol. 34, 9–16 (2000).Article 
CAS 
PubMed 

Google Scholar 
Toma, M. A. et al. Tripartite symbiosis of Sophora tomentosa, rhizobia and arbuscular mycorhizal fungi. Braz. J. Microbiol. 48, 680–688 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elanchezhian, R., Rajalakshmi, S. & Jayakumar, V. Salt tolerance characteristics of rhizobium species associated with Vigna marina. Indian J. Agric. Sci. 79, 980–985 (2009).CAS 

Google Scholar 
Chapin, F. S., Matson, P. A., Mooney, H. A. & Vitousek, P. M. Principles of Terrestrial Ecosystem Ecology (Springer, 2002).Vitousek, P. M., Walker, L. R., Whiteaker, L. D. & Matson, P. A. Nutrient limitations to plant growth during primary succession in Hawaii Volcanoes National Park. Biogeochemistry 23, 197–215 (1993).Article 

Google Scholar 
Liao, C. et al. Altered ecosystem carbon and nitrogen cycles by plant invasion: a meta-analysis. N. Phytologist 177, 706–714 (2008).Article 
CAS 

Google Scholar 
Woodward, S. A. et al. Use of the Exotic Tree Myrica Faya by Native and Exotic Birds in Hawai’i Volcanoes National Park (University of Hawaii Press, 1990).Vitousek, P. M., Walker, L. R., Whiteaker, L. D., Mueller-Dombois, D. & Matson, P. A. Biological invasion by Myrica faya alters ecosystem development in Hawaii. Science 238, 802–804 (1987).Article 
CAS 
PubMed 

Google Scholar 
Theoharides, K. A. & Dukes, J. S. Plant invasion across space and time: factors affecting nonindigenous species success during four stages of invasion. N. phytologist 176, 256–273 (2007).Article 

Google Scholar 
Kalwij, J. M. Review of ‘The Plant List, a working list of all plant species’. J. Vegetation Sci. 23, 998–1002 (2012).Article 

Google Scholar 
Byng, J. W. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Botanical J. Linn. Soc. 181, 1–20 (2016).Article 

Google Scholar 
Soudzilovskaia, N. A. et al. FungalRoot: Global online database of plant mycorrhizal associations. N. Phytol. 227, 955–966 (2020).Article 

Google Scholar 
Weigelt, P., König, C. & Kreft, H. GIFT–A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Danielson, J. J. & Gesch, D. B. “Global multi-resolution terrain elevation data 2010 (GMTED2010),” (US Geological Survey, 2011).Weigelt, P. & Kreft, H. Quantifying island isolation–insights from global patterns of insular plant species richness. Ecography 36, 417–429 (2013).Article 

Google Scholar 
Kreft, H., Jetz, W., Mutke, J., Kier, G. & Barthlott, W. Global diversity of island floras from a macroecological perspective. Ecol. Lett. 11, 116–127 (2008).PubMed 

Google Scholar 
Triantis, K. A., Economo, E. P., Guilhaumon, F. & Ricklefs, R. E. Diversity regulation at macro‐scales: species richness on oceanic archipelagos. Glob. Ecol. Biogeogr. 24, 594–605 (2015).Article 

Google Scholar 
Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).Article 

Google Scholar 
Bivand, R. R packages for analyzing spatial data: a comparative case study with areal data. Geogr. Anal. 54, 488–518 (2022).Article 

Google Scholar 
R. C. Team, R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar  More

Suttle CA. Marine viruses-major players in the global ecosystem. Nat Rev Microbiol. 2007;5:801–12.CAS 
PubMed 

Google Scholar 
Fuhrman JA. Marine viruses and their biogeochemical and ecological effects. Nature 1999;399:541–8.CAS 
PubMed 

Google Scholar 
Rohwer F, Thurber RV. Viruses manipulate the marine environment. Nature 2009;459:207–12.CAS 
PubMed 

Google Scholar 
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
PubMed 

Google Scholar 
Zimmerman AE, Howard-Varona C, Needham DM, John SG, Worden AZ, Sullivan MB, et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat Rev Microbiol. 2020;18:21–34.CAS 
PubMed 

Google Scholar 
Rosenwasser S, Ziv C, Creveld SGV, Vardi A. Virocell metabolism: metabolic innovations during host-virus interactions in the ocean. Trends Microbiol. 2016;24:821–32.CAS 
PubMed 

Google Scholar 
Fuchsman CA, Carlson MCG, Garcia Prieto D, Hays MD, Rocap G. Cyanophage host-derived genes reflect contrasting selective pressures with depth in the oxic and anoxic water column of the Eastern Tropical North Pacific. Environ Microbiol. 2021;23:2782–2800.CAS 
PubMed 

Google Scholar 
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 2016;537:689–93.CAS 
PubMed 

Google Scholar 
Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro-and microdiversity from pole to pole. Cell 2019;177:1109–23.CAS 
PubMed 
PubMed Central 

Google Scholar 
Brum JR, Ignacio-Espinoza JC, Roux S, Doulcier G, Acinas SG, Alberti A, et al. Patterns and ecological drivers of ocean viral communities. Science 2015;348:1261498.PubMed 

Google Scholar 
Dion MB, Oechslin F, Moineau S. Phage diversity, genomics and phylogeny. Nat Rev Microbiol. 2020;18:125–38.CAS 
PubMed 

Google Scholar 
Sullivan MB, Waterbury JB, Chisholm SW. Cyanophages infecting the oceanic cyanobacterium Prochlorococcus. Nature 2003;424:1047–51.CAS 
PubMed 

Google Scholar 
Mann NH. Phages of the marine cyanobacterial picophytoplankton. FEMS Microbiol Rev. 2003;27:17–34.CAS 
PubMed 

Google Scholar 
Ni T, Zeng Q. Diel infection of cyanobacteria by cyanophages. Front Mar Sci. 2016;2:123.
Google Scholar 
Flombaum P, Gallegos JL, Gordillo RA, Rincon J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA 2013;110:9824–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Biller SJ, Berube PM, Lindell D, Chisholm SW. Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol 2015;13:13–27.CAS 
PubMed 

Google Scholar 
Proctor LM, Fuhrman JA. Viral mortality of marine-bacteria and cyanobacteria. Nature 1990;343:60–62.
Google Scholar 
Carlson MCG, Ribalet F, Maidanik I, Durham BP, Hulata Y, Ferron S, et al. Viruses affect picocyanobacterial abundance and biogeography in the North Pacific Ocean. Nat Microbiol 2022;7:570–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Matteson AR, Loar SN, Pickmere S, DeBruyn JM, Ellwood MJ, Boyd PW, et al. Production of viruses during a spring phytoplankton bloom in the South Pacific Ocean near of New Zealand. FEMS Microbiol Ecol 2012;79:709–19.CAS 
PubMed 

Google Scholar 
Ribalet F, Swalwell J, Clayton S, Jimenez V, Sudek S, Lin Y, et al. Light-driven synchrony of Prochlorococcus growth and mortality in the subtropical Pacific gyre. Proc Natl Acad Sci USA. 2015;112:8008–12.CAS 
PubMed 
PubMed Central 

Google Scholar 
Demory D, Liu R, Chen Y, Zhao F, Coenen AR, Zeng Q, et al. Linking light-dependent life history traits with population dynamics for Prochlorococcus and cyanophage. mSystems 2020;5:e00586–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D. Genomic island variability facilitates Prochlorococcus-virus coexistence. Nature 2011;474:604–8.CAS 
PubMed 

Google Scholar 
Marston MF, Pierciey FJ Jr, Shepard A, Gearin G, Qi J, Yandava C, et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc Natl Acad Sci USA 2012;109:4544–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiao X, Guo W, Li X, Wang C, Chen X, Lin X, et al. Viral lysis alters the optical properties and biological availability of dissolved organic matter derived from Prochlorococcus picocyanobacteria. Appl Environ Microbiol. 2021;87:e02271–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiao X, Zeng Q, Zhang R, Jiao N. Prochlorococcus viruses—From biodiversity to biogeochemical cycles. Sci China Earth Sci. 2018;61:1728–36.
Google Scholar 
Jover LF, Effler TC, Buchan A, Wilhelm SW, Weitz JS. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat Rev Microbiol. 2014;12:519–28.CAS 
PubMed 

Google Scholar 
Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Viruses inhibit CO2 fixation in the most abundant phototrophs on earth. Curr Biol 2016;26:1585–9.CAS 
PubMed 

Google Scholar 
Weitz JS, Stock CA, Wilhelm SW, Bourouiba L, Coleman ML, Buchan A, et al. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J. 2015;9:1352–64.PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Coleman ML, Weigele P, Rohwer F, Chisholm SW. Three Prochlorococcus cyanophage genomes: signature features and ecological interpretations. PLoS Biol. 2005;3:e144.PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Krastins B, Hughes JL, Kelly L, Chase M, Sarracino D, et al. The genome and structural proteome of an ocean siphovirus: a new window into the cyanobacterial ‘mobilome’. Environ Microbiol. 2009;11:2935–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Huang KH, Ignacio-Espinoza JC, Berlin AM, Kelly L, Weigele PR, et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ Microbiol. 2010;12:3035–56.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sabehi G, Shaulov L, Silver DH, Yanai I, Harel A, Lindell D. A novel lineage of myoviruses infecting cyanobacteria is widespread in the oceans. Proc Natl Acad Sci USA 2012;109:2037–42.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Wang K, Jiao N, Chen F. Genome sequences of siphoviruses infecting marine Synechococcus unveil a diverse cyanophage group and extensive phage-host genetic exchanges. Environ Microbiol. 2012;14:540–58.CAS 
PubMed 

Google Scholar 
Labrie SJ, Frois-Moniz K, Osburne MS, Kelly L, Roggensack SE, Sullivan MB, et al. Genomes of marine cyanopodoviruses reveal multiple origins of diversity. Environ Microbiol. 2013;15:1356–76.CAS 
PubMed 

Google Scholar 
Dekel-Bird NP, Avrani S, Sabehi G, Pekarsky I, Marston MF, Kirzner S, et al. Diversity and evolutionary relationships of T7-like podoviruses infecting marine cyanobacteria. Environ Microbiol. 2013;15:1476–91.CAS 
PubMed 

Google Scholar 
Huang S, Wilhelm SW, Jiao N, Chen F. Ubiquitous cyanobacterial podoviruses in the global oceans unveiled through viral DNA polymerase gene sequences. ISME J. 2010;4:1243–51.PubMed 

Google Scholar 
Baran N, Goldin S, Maidanik I, Lindell D. Quantification of diverse virus populations in the environment using the polony method. Nat Microbiol. 2018;3:62–72.CAS 
PubMed 

Google Scholar 
Chow C-ET, Suttle CA. Biogeography of viruses in the sea. Annu Rev Virol. 2015;2:41–66.CAS 
PubMed 

Google Scholar 
Chen F, Lu JR. Genomic sequence and evolution of marine cyanophage P60: a new insight on lytic and lysogenic phages. Appl Environ Microbiol. 2002;68:2589–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Zhang S, Jiao N, Chen F. Comparative genomic and phylogenomic analyses reveal a conserved core genome shared by estuarine and oceanic cyanopodoviruses. PLoS One. 2015;10:e0142962.PubMed 
PubMed Central 

Google Scholar 
Pope WH, Weigele PR, Chang J, Pedulla ML, Ford ME, Houtz JM, et al. Genome sequence, structural proteins, and capsid organization of the cyanophage Syn5: A “horned’ bacteriophage of marine Synechococcus. J Mol Biol. 2007;368:966–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Sun Y, Zhang S, Long L. Temporal transcriptomes of a marine cyanopodovirus and its Synechococcus host during infection. Microbiologyopen 2021;10:e1150.CAS 
PubMed 

Google Scholar 
Wang K, Chen F. Prevalence of highly host-specific cyanophages in the estuarine environment. Environ Microbiol. 2008;10:300–12.CAS 
PubMed 

Google Scholar 
Chen F, Wang K, Huang S, Cai H, Zhao M, Jiao N, et al. Diverse and dynamic populations of cyanobacterial podoviruses in the Chesapeake Bay unveiled through DNA polymerase gene sequences. Environ Microbiol. 2009;11:2884–92.PubMed 

Google Scholar 
Goldin S, Hulata Y, Baran N, Lindell D. Quantification of T4-like and T7-like cyanophages using the polony method show they are significant members of the virioplankton in the North Pacific Subtropical Gyre. Front Microbiol. 2020;11:1210.PubMed 
PubMed Central 

Google Scholar 
Nasko DJ, Chopyk J, Sakowski EG, Ferrell BD, Polson SW, Wommack KE. Family A DNA polymerase phylogeny uncovers diversity and replication gene organization in the virioplankton. Front Microbiol. 2018;9:3053.PubMed 
PubMed Central 

Google Scholar 
Dekel-Bird NP, Sabehi G, Mosevitzky B, Lindell D. Host-dependent differences in abundance, composition and host range of cyanophages from the Red Sea. Environ Microbiol. 2015;17:1286–99.CAS 
PubMed 

Google Scholar 
Hanson CA, Marston MF, Martiny JBH. Biogeographic variation in host range phenotypes and taxonomic composition of marine cyanophage isolates. Front Microbiol. 2016;7:983.PubMed 
PubMed Central 

Google Scholar 
Rocap G, Larimer FW, Lamerdin J, Malfatti S, Chain P, Ahlgren NA, et al. Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 2003;424:1042–7.CAS 
PubMed 

Google Scholar 
Chen B, Wang L, Song S, Huang B, Sun J, Liu H. Comparisons of picophytoplankton abundance, size, and fluorescence between summer and winter in northern South China Sea. Cont Shelf Res. 2011;31:1527–40.
Google Scholar 
Lindell D, Jaffe JD, Coleman ML, Futschik ME, Axmann IM, Rector T, et al. Genome-wide expression dynamics of a marine virus and host reveal features of co-evolution. Nature 2007;449:83–86.CAS 
PubMed 

Google Scholar 
Zhao Y, Qin F, Zhang R, Giovannoni SJ, Zhang Z, Sun J, et al. Pelagiphages in the Podoviridae family integrate into host genomes. Environ Microbiol. 2019;21:1989–2001.CAS 
PubMed 

Google Scholar 
Leptihn S, Gottschalk J, Kuhn A. T7 ejectosome assembly: A story unfolds. Bacteriophage 2016;6:e1128513.CAS 
PubMed 
PubMed Central 

Google Scholar 
Thompson LR, Zeng Q, Kelly L, Huang KH, Singer AU, Stubbe J, et al. Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc Natl Acad Sci USA 2011;108:E757–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zeng Q, Chisholm SW. Marine viruses exploit their host’s two-component regulatory system in response to resource limitation. Curr Biol 2012;22:124–8.CAS 
PubMed 

Google Scholar 
Zeng Q, Bonocora RP, Shub DA. A free-standing homing endonuclease targets an intron insertion site in the psbA gene of cyanophages. Curr Biol. 2009;19:218–22.CAS 
PubMed 

Google Scholar 
Lindell D, Jaffe JD, Johnson ZI, Church GM, Chisholm SW. Photosynthesis genes in marine viruses yield proteins during host infection. Nature 2005;438:86–89.CAS 
PubMed 

Google Scholar 
Breitbart M, Thompson LR, Suttle CA, Sullivan MB. Exploring the vast diversity of marine viruses. Oceanography. 2007;20:135–9.
Google Scholar 
Kazlauskas D, Venclovas C. Computational analysis of DNA replicases in double-stranded DNA viruses: relationship with the genome size. Nucleic Acids Res. 2011;39:8291–305.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu X, Zhang Q, Murata K, Baker ML, Sullivan MB, Fu C, et al. Structural changes in a marine podovirus associated with release of its genome into Prochlorococcus. Nat Struct Mol Biol. 2010;17:830–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Dai W, Fu C, Raytcheva D, Flanagan J, Khant HA, Liu XG, et al. Visualizing virus assembly intermediates inside marine cyanobacteria. Nature 2013;502:707–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu R, Liu Y, Chen Y, Zhan Y, Zeng Q. Cyanobacterial viruses exhibit diurnal rhythms during infection. Proc Natl Acad Sci USA 2019;116:14077–82.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maidanik I, Kirzner S, Pekarski I, Arsenieff L, Tahan R, Carlson MCG, et al. Cyanophages from a less virulent clade dominate over their sister clade in global oceans. ISME J. 2022;16:2169–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shitrit D, Hackl T, Laurenceau R, Raho N, Carlson MCG, Sabehi G, et al. Genetic engineering of marine cyanophages reveals integration but not lysogeny in T7-like cyanophages. ISME J. 2022;16:488–99.CAS 
PubMed 

Google Scholar 
Liang Y, Wang L, Wang Z, Zhao J, Yang Q, Wang M, et al. Metagenomic analysis of the diversity of DNA viruses in the surface and deep sea of the South China Sea. Front Microbiol. 2019;10:1951.PubMed 
PubMed Central 

Google Scholar 
Pedrós-Alió C, Potvin M, Lovejoy C. Diversity of planktonic microorganisms in the Arctic Ocean. Prog Oceanogr. 2015;139:233–43.
Google Scholar 
Luo E, Eppley JM, Romano AE, Mende DR, DeLong EF. Double-stranded DNA virioplankton dynamics and reproductive strategies in the oligotrophic open ocean water column. ISME J. 2020;14:1304–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
Steidinger BS, Crowther TW, Liang J, Van Nuland ME, Werner GDA, Reich PB, et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 2019;569:404–8.CAS 
PubMed 

Google Scholar 
Xie X, Wu T, Zhu M, Jiang G, Xu Y, Wang X, et al. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol Indic. 2021;120:106925.CAS 

Google Scholar 
Lee SJ, Richardson CC. Choreography of bacteriophage T7 DNA replication. Curr Opin Chem Biol. 2011;15:580–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kulczyk AW, Richardson CC. The replication system of bacteriophage T7. Enzymes. 2016;39:89–136.CAS 
PubMed 

Google Scholar 
Benkovic SJ, Valentine AM, Salinas F. Replisome-mediated DNA replication. Annu Rev Biochem. 2001;70:181–208.CAS 
PubMed 

Google Scholar 
Johnson A, O’Donnell M. Cellular DNA replicases: components and dynamics at the replication fork. Annu Rev Biochem. 2005;74:283–315.CAS 
PubMed 

Google Scholar 
Seco EM, Zinder JC, Manhart CM, Lo Piano A, McHenry CS, Ayora S. Bacteriophage SPP1 DNA replication strategies promote viral and disable host replication in vitro. Nucleic Acids Res. 2013;41:1711–21.CAS 
PubMed 

Google Scholar 
Mruwat N, Carlson MCG, Goldin S, Ribalet F, Kirzner S, Hulata Y, et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 2021;15:41–54.CAS 
PubMed 

Google Scholar 
Moore LR, Rocap G, Chisholm SW. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 1998;393:464–7.CAS 
PubMed 

Google Scholar 
Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Shedding new light on viral photosynthesis. Photosynth Res. 2015;126:71–97.CAS 
PubMed 

Google Scholar 
Edwards KF, Steward GF, Schvarcz CR. Making sense of virus size and the tradeoffs shaping viral fitness. Ecol Lett. 2021;24:363–73.PubMed 

Google Scholar 
Moore LR, Coe A, Zinser ER, Saito MA, Sullivan MB, Lindell D, et al. Culturing the marine cyanobacterium Prochlorococcus. Limnol Oceanogr Methods. 2007;5:353–62.CAS 

Google Scholar 
Hyman P, Abedon ST. Bacteriophage host range and bacterial resistance. Adv Appl Microbiol. 2010;70:217–48.CAS 
PubMed 

Google Scholar 
Fridman S, Flores-Uribe J, Larom S, Alalouf O, Liran O, Yacoby I, et al. A myovirus encoding both photosystem I and II proteins enhances cyclic electron flow in infected Prochlorococcus cells. Nat Microbiol. 2017;2:1350–7.CAS 
PubMed 

Google Scholar 
Fang X, Liu Y, Zhao Y, Chen Y, Liu R, Qin QL, et al. Transcriptomic responses of the marine cyanobacterium Prochlorococcus to viral lysis products. Environ Microbiol. 2019;21:2015–28.CAS 
PubMed 

Google Scholar 
John SG, Mendez CB, Deng L, Poulos B, Kauffman AK, Kern S, et al. A simple and efficient method for concentration of ocean viruses by chemical flocculation. Environ Microbiol Rep. 2011;3:195–202.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 2011;27:863–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:1–10.
Google Scholar 
Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.CAS 
PubMed 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS 
PubMed 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic Inference in the genomic era. Mol Biol Evol. 2020;37:2461–2461.PubMed 
PubMed Central 

Google Scholar 
Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
Martinez-Hernandez F, Fornas O, Lluesma Gomez M, Bolduc B, de la Cruz Pena MJ, Martinez JM, et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat Commun. 2017;8:15892.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang Z, Qin F, Chen F, Chu X, Luo H, Zhang R, et al. Culturing novel and abundant pelagiphages in the ocean. Environ Microbiol 2021;23:1145–61.CAS 
PubMed 

Google Scholar 
Buchholz HH, Michelsen ML, Bolanos LM, Browne E, Allen MJ, Temperton B. Efficient dilution-to-extinction isolation of novel virus-host model systems for fastidious heterotrophic bacteria. ISME J. 2021;15:1585–98.CAS 
PubMed 
PubMed Central 

Google Scholar 
Qin F, Du S, Zhang Z, Ying H, Wu Y, Zhao G, et al. Newly identified HMO-2011-type phages reveal genomic diversity and biogeographic distributions of this marine viral group. ISME J. 2022;16:1363–75.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Data processingIn 2019, the CETUS data spanning between 2012 and 2017 was published open access through the Flanders Marine Institute (VLIZ) IPT portal and distributed by EMODnet and OBIS, in a first version of the CETUS dataset9. The data collected between 2018 and 2019 was prepared as the 2012–2017 data9. Methods for photographic verification/validation and to evaluate the MMOs experience were applied (see below), in order to include new variables on data quality in an updated version of the dataset. Currently, the CETUS dataset is updated, with a 2nd version available10. It comprises data from 2012 to 2017, with the following two new columns on the observers’ experience: “most experienced observer” and “least experienced observer”; and a new column associated with validation of the sightings’ identifications: “photographic validation”. The results here presented correspond to the analysis of the data from 2012 to 2019, and the open-access dataset will soon be further updated with the 2018–2019 data.Photographic verificationAll the former MMOs who have integrated the CETUS Project, between 2012 and 2019, were contacted and asked to provide any available photographic or video records of cetaceans collected during their on board periods. The collection of sighting’s images was not a requirement of the CETUS protocol, and so these records were obtained opportunistically, with availability and quality depending on several factors: observers on board having personal cameras, camera quality, intention of the observer taking the photograph (e.g., for aesthetic or identification purposes).The images obtained were organized in a folder hierarchy from the year to the day of recording. However, not all the images had metadata up to the day of recording, so these were inserted into the most appropriate hierarchy-level of the folder organization. For each set of records corresponding to a single-taxon sighting, the photos/videos with the better quality or framing (i.e., that allowed for an easier species identification) were selected for that sighting. The remaining photos/videos were only consulted in case of doubt (e.g., to look for additional details that could help with the identification).Verification consisted of the process of matching the photographic/video records with the dataset sighting registers. Whenever possible and ideally, the file metadata was used for the process. However, often, the date and/or time of the file metadata were wrong, non-existent, or in different time zones. In these cases, a conservative methodology was applied using all available information to match as many sightings as possible. An estimation of time lag was attempted (based on, at least, two obvious matches between photographs/videos and dataset registers, e.g., unique sighting of the day, close to the boat, easy/obvious identification). When not possible, further evaluation consisted in assessing whether the sighting and image record was too obvious, and accounting for unique complementary information on the sighting (e.g., the number of animals or the side of the sighting were unique for that day and/or for that species/group).Photographic validationAfter the verification process, the validation of the matched records was carried out, to confirm or correct the species identification of sightings in the 1st version of the CETUS dataset (i.e., reported by the MMOs on board). The validation approach involved, for more dubious identification through the photo/video records, the discussion between four experienced observers of the CETUS team. In cases where no consensual agreement was achieved, an external expert on cetacean identification was also consulted. Identifications made through the photographic/video records required 100% certainty, and these were then compared with the cetacean identifications provided in the 1st version of the CETUS dataset. Then, the occurrence records with originally misidentifications of cetaceans, as well as those records where validation allowed to achieve an identification to a lower taxon, were corrected in the 2nd version of the dataset (i.e., a delphinid sighting validated as common dolphin, will now appear as common dolphin). A new column “photographic validation” was added to the dataset with the following categories: “yes” (i.e., validated with photograph/video), “no” (i.e., not validated with photograph/video), and “to the family” (i.e., validation only to the family taxon).For further analysis, specifically for the model process on the identification success (see below), registers were considered “completely validated” if it was possible to complete the photographic/video identification process up to the species level (then, differentiating if the original identification from the MMOs was or not correct). For Globicephala sp. and Kogia sp., validation to the genus was considered complete, since the species from both genera are visually hardly differentiated, especially at sea.Creating a data quality criteria: evaluating MMOs experienceQuality criteria were created to evaluate the MMOs experience based on the information collected from their curricula vitae (CVs) (alumni MMOs provided as many CVs as the years of their participation in CETUS). The following quality criteria were considered: (i) the experience at sea, (ii) the experience with cetaceans’ ID, (iii) the number of species they have worked with, and (iv) the experience working with the CETUS Project protocol. Each of these quality criteria was ranked from 0 to 5, and then these were summed to generate an evaluation score, on a scale of 0 to 20, attributed to each MMO (Table 4).Table 4 Quality criteria for MMOs evaluation.Full size tableThe MMOs evaluations were computed for each cruise (i.e., the trip from one port to another), considering the experience of the MMOs based on the CV obtained for that year, plus the experience acquired during CETUS participation in previous cruises that year. Since most of the times, the team of observers on board each cruise was constituted by two MMOs, two final evaluation scores were attributed to each cruise in the 2nd version of the CETUS dataset, into two new columns: “most experienced observer” and “least experienced observer”. On rare occasions where there is only one observer on board that cruise, only the evaluation of the single observer was included under the column “most experienced observer”, leaving the column “least experienced observer” as “NULL”. To investigate the experience of MMOs on board, both individually and cumulative (LEO + MEO), the combination of the score values was computed by cruise. These were then trimmed to unique combinations of evaluation scores.The names of observers, previously presented in the online dataset for each cruise, were removed for anonymity purposes, as there is now ancillary information regarding their experience.Model fittingTwo Generalized Additive Models (GAM) were fitted to assess bias on the number of sightings recorded per survey and on the identification success of cetacean species. Details for each model are presented below. Both models were fitted in R (Version 4.1.0). Prior to modelling, Pearson correlations were calculated between all pairs of explanatory variables, considered for each model (see below), to exclude highly correlated variables, considering a threshold of 0.7524,25,28. Since the variables regarding MMOs’ experience were correlated (LEO or MEO correlated with cumulative and mean experience; and cumulative experience correlated with mean experience – Supplementary Fig. S3), these variables were not included in the first fitting stage (backward selection) but included later through forward selection (see below). Multicollinearity among explanatory variables was measured through the Variance Inflation Factor (VIF), with a threshold of 3 (Supplementary Tables S4)24,25,29. After removing the MMOs evaluation scores, no multicollinearity was observed, so all the other variables were kept for the first fitting stage.For model selection, a backward selection was applied to oversaturated models18,24,25,30,31. The Akaike Information Criterion (AIC) was used as a measure of adequation of fitness, choosing the model with the lowest AIC value at each step of the model fitting process, i.e., comparing nested models (larger model incorporating one more explanatory variable compared with the smaller model). If the AIC-difference between the two models was less than 2, an Analysis of Variance (ANOVA), through chi-square test, was used to check if the AIC-difference was significant24,25,32. If this difference was not statistically significant (p  > 0.05), the simplest model (smaller model) was kept. Through a forward selection process, the variables regarding the MMOs evaluation scores were added, one at a time, to the best model obtained in the previous backward selection. After comparing the models with each other (separate variables for LEO + MEO vs. Cumulative Evaluation vs Mean Evaluation), the best model, considering the AIC value, was kept. A final backward selection process was then applied.All GAMs were fitted with the “mgcv” package (https://cran.r-project.org/web/packages/mgcv) and a maximum of four splines (k = 4) was chosen to limit the complexity of smoothers describing the effects of the explanatory variables25,31. If a spline was close to linear (with estimated degrees of freedom of ~1), the smooth term was removed, and a linear function was fitted. To check for model quality, the “gam.check” function was used to verify the diagnostic plots and the adequacy of the number of splines (Supplementary Figs. S5 and S6). Existence of influential data points was assessed (with the threshold of 0.25 for the Hat values), as well as the correlation between model residuals and explanatory variables. In both final models, number of splines was adequate and there were no influential data points or clear correlation between residuals and explanatory variables (Supplementary Figs. S7 and S8)24,32.Bias modelling of number of sightingsTo assess the bias parameters on the number of sightings recorded per survey (i.e., a full day monitoring, from sunrise to sunset), the following detectability factors were considered as explanatory variables: weather conditions (i.e., the minimums and maximums of the sea state, wind state, and visibility), the experience of MMOs (i.e., the evaluation scores of the least and the most experienced observers, as well as the mean and cumulative evaluations of the MMOs experience) and kilometres sampled “on-effort” (i.e., periods of active survey). Sampling periods were divided into “On-effort” and “Off-effort” conditions, based on four meteorological variables: sea state (Douglas scale), wind state (Beaufort scale), visibility (measured in a categorical scale ranging from 0–10 and estimated from the distance to the horizon line and possible reference points at a known range, e.g., ships with an automatic identification system,  > 1000 km), and the occurrence of rain (see Supplementary Table S9)10. For the model fitting, only “on-effort” periods of sampling were considered. Given that the response variable was count data, a Poisson distribution was tested (with a log link function). Then, the resulting first oversaturated model was checked for overdispersion, through a Pearson estimator. Since it tested positive for overdispersion (φ = 1.99), a negative binomial distribution (with a log link function) was fitted.Bias modelling of identification successA binary response variable, based on the success in the species identification for each sighting, was generated, and a model with binomial distribution (with a logit link function) was fitted. As in the previous model, only “on-effort” records were used. The total number of non-successful identifications across the dataset (the 0 s of the model) was extrapolated from the proportion of wrong identifications obtained in the validation process. To calculate this proportion, only complete validated sightings registered “on-effort” were used. Proportions were computed and extrapolated to Odontoceti and Mysticeti, separately. This resulted in 78 non-successful identifications in delphinids, plus 17 misidentifications in baleen whales, i.e., a total of 95 “on-effort” sightings randomly selected from the dataset were defined as unsuccessful identifications (0 s in the response variable for model fitting). The remaining records were considered successful identifications (1 s in the response variable for model fitting). To assess the bias parameters on the identification success, the following independent variables were considered in the analysis: the group (i.e., Group A: Odontoceti sightings, excluding sperm whale (Physeter macrocephalus); and Group B: Mysticeti sightings, plus sperm whale), the size of the group (i.e., the best estimate of the number of animals in a sighting, from the observer’s perspective), sighting distance (i.e., a relative measure according to the scale of the binoculars), weather conditions (i.e., the sea state, wind state, and visibility at the time of each sighting), the experience of MMOs (i.e., the evaluation scores of least and most experienced observers, as well as the mean and cumulative scores of the MMOs teams). Group A and B were settled according to cetacean morphology. However, since sperm whales have closer similarities with Mysticeti species, they were also included in Group B21,33. This categorization was mostly based on body size, as this is likely the main factor, regarding the species morphology, influencing the identification. Group A is constituted by species with a medium length of less than 10 meters, while Group B includes the larger species over 10 meters (Mysticeti plus P. macrocephalus)33. Since in the CETUS Project, different binoculars have been used – with two different reticle scales – it was necessary to standardize binocular distances to the same scale. More