Ecology

Bergé, J.-P. & Barnathan, G. Fatty acids from lipids of marine organisms: Molecular biodiversity, roles as biomarkers, biologically active compounds, and economical aspects. Adv. Biochem. Eng. Biotechnol. 96, 49–125 (2005).
Google Scholar 
Parzanini, C., Parrish, C., Hamel, J. & Mercier, A. Functional diversity and nutritional content in a deep-sea faunal assemblage through total lipid, lipid class, and fatty acid analyses. PLoS ONE 13, e0207395 (2018).Article 

Google Scholar 
Parrish, C. C. Lipids in marine ecosystems. ISRN Oceanogr. 2013, 1–16 (2013).Article 

Google Scholar 
Parrish, C. et al. Lipid and phenolic biomarkers in marine ecosystems: analysis and applications. In Marine Chemistry. The Handbook of Environmental Chemistry (Vol. 5 Series: Water Pollution) Vol. 5 (ed. Wangersky, P. J.) (Springer, 2000).
Google Scholar 
Laender, F. D., Oevelen, D. V., Frantzen, S., Middelburg, J. J. & Soetaert, K. Seasonal PCB bioaccumulation in an arctic marine ecosystem: a model analysis incorporating lipid dynamics, food-web productivity and migration. Environ. Sci. Technol. 44, 356–361 (2010).Article 

Google Scholar 
Bianchi, T. & Canuel, E. Chemical Biomarkers in Aquatic Ecosystems (Princeton University Press, 2011).Book 

Google Scholar 
Signa, G. et al. Lipid and fatty acid biomarkers as proxies for environmental contamination in caged mussels Mytilus galloprovincialis. Ecol. Indic. 57, 384–394 (2015).CAS 
Article 

Google Scholar 
Brett, M., Mueller-Navarra, D. & Persson, J. Crustacean zooplankton fatty acid composition. In Lipids in Aquatic Ecosystems (eds Kainz, M. et al.) 115–146 (Springer, 2009).Chapter 

Google Scholar 
Martin-Creuzburg, D. & Elert, E. Ecological significance of sterols in aquatic food webs. In Lipids in Aquatic Ecosystems (eds Kainz, M. et al.) 43–64 (Springer, 2009).Chapter 

Google Scholar 
Parrish, C. Essential fatty acids in aquatic food webs. In Lipids in Aquatic Ecosystem (eds Kainz, M. et al.) 309–326 (Springer, 2009).Chapter 

Google Scholar 
Maier, S. R., Bannister, R. J., van Oevelen, D. & Kutti, T. Seasonal controls on the diet, metabolic activity, tissue reserves and growth of the cold-water coral Lophelia pertusa. Coral Reefs 39, 173–187 (2020).Article 

Google Scholar 
Phleger, C. F. Buoyancy in marine fishes: Direct and indirect role of lipids. Am. Zool. 38, 321–330 (1998).CAS 
Article 

Google Scholar 
Pond, D. W. & Tarling, G. A. Phase transitions of wax esters adjust buoyancy in diapausing Calanoides acutus. Limnol. Oceanogr. 56, 1310–1318 (2011).CAS 
Article 

Google Scholar 
Giese, A. C. Lipids in the economy of marine invertebrates. Physiol. Rev. 46, 244–298 (1966).CAS 
Article 

Google Scholar 
Joseph, J. D. Distribution and composition of lipids in marine invertebrates. In Marine Biogenic Lipids, Fats and Oils (ed. Ackman, R. G.) 49–143 (CRC Press, 1989).
Google Scholar 
Lee, R. F. Lipoproteins from the hemolymph and ovaries of marine invertebrates. In Advances in Comparative and Environmental Physiology (eds Houlihan, D. F. et al.) 187–207 (Springer, 1991).Chapter 

Google Scholar 
Kattner, G. & Hagen, W. Lipid metabolism of the Antarctic euphausiid Euphausia crystallorophias and its ecological implications. Mar. Ecol. Prog. Ser. 170, 203–213 (1998).CAS 
Article 

Google Scholar 
Heras, H., Pollero, R. J., Gonzalez-Baró, M. R. & Pollero, R. J. Lipid and fatty acid composition and energy partitioning during embryo development in the shrimp Macrobrachium borellii. Lipids 35, 645–651 (2000).CAS 
Article 

Google Scholar 
Viladrich, N. et al. Variation in lipid and free fatty acid content during spawning in two temperate octocorals with different reproductive strategies: surface versus internal brooder. Coral Reefs 35, 1033–1045 (2016).Article 

Google Scholar 
Hansen, M., Flatt, T. & Aguilaniu, H. Reproduction, fat metabolism, and lifespan—What is the connection?. Cell Metab. 17, 10–19 (2013).CAS 
Article 

Google Scholar 
Strathmann, R. R. Egg size, larval development, and juvenile size in benthic marine invertebrates. Am. Nat. 111, 373–376 (1977).Article 

Google Scholar 
Pechenik, J. Delayed metamorphosis by larvae of benthic marine-invertebrates—Does it occur? Is there a price to pay?. Ophelia 32, 63–94 (1990).Article 

Google Scholar 
Harms, J. Larval development and delayed metamorphosis in the hermit crab Clibanarius erythropus (Latreille) (Crustacea, Diogenidae). J. Exp. Mar. Bio. Ecol. 156, 151–160 (1992).Article 

Google Scholar 
Harii, S., Kayanne, H., Takigawa, H. T., Hayashibara, T. H. & Yamamoto, M. Larval survivorship, competency periods and settlement of two brooding corals, Heliopora coerulea and Pocillopora damicornis. Mar. Biol. 141, 39–46 (2002).Article 

Google Scholar 
Doughty, P. & Shine, R. Detecting life history trade-offs: measuring energy stores in “capital” breeders reveals costs of reproduction. Oecologia 110, 508–513 (1997).Article 

Google Scholar 
Coma, R., Ribes, M., Gili, J.-M. & Zabala, M. An energetic approach to the study of life-history traits of two modular colonial benthic invertebrates. Mar. Ecol. Prog. Ser. 162, 89–103 (1998).Article 

Google Scholar 
Rossi, S. et al. Temporal variation in protein, carbohydrate, and lipid concentrations in Paramuricea clavata (Anthozoa, Octocorallia): Evidence for summer–autumn feeding constraints. Mar. Biol. 149, 643–651 (2006).CAS 
Article 

Google Scholar 
Kattner, G., Graeve, M. & Hagen, W. Ontogenetic and seasonal changes in lipid and fatty acid/alcohol compositions of the dominant Antarctic copepods Calanus propinquus, Calanoides acutus and Rhincalanus gigas. Mar. Biol. 644, 18119 (1994).
Google Scholar 
Lee, R. F., Hagen, W. & Kattner, G. Lipid storage in marine zooplankton. Mar. Ecol. Prog. Ser. 307, 273–306 (2006).CAS 
Article 

Google Scholar 
Mourente, G., Medina, A., González, S. & Rodríguez, A. Variations in lipid content and nutritional status during larval development of the marine shrimp Penaeus kerathurus. Aquaculture 130, 187–199 (1995).CAS 
Article 

Google Scholar 
Marshall, C. T., Yaragina, N. A., Lambert, Y. & Kjesbu, O. S. Total lipid energy as a proxy for total egg production by fish stocks. Nature 402, 288–290 (1999).CAS 
Article 

Google Scholar 
Marshall, C. T., Yaragina, N. A., Ådlandsvik, B. & Dolgov, A. V. Reconstructing the stock-recruit relationship for Northeast Arctic cod using a bioenergetic index of reproductive potential. Can. J. Fish. Aquat. Sci. 57, 2433–2442 (2000).Article 

Google Scholar 
Dalsgaard, J., St. John, M., Kattner, G., Müller-Navarra, D. & Hagen, W. B. Fatty acid trophic markers in the pelagic marine environment. Adv. Mar. Biol. 46, 225–340 (2003).Article 

Google Scholar 
Bergquist, P. R., Lawson, M. P., Lavis, A. & Cambie, R. C. Fatty acid composition and the classification of the Porifera. Biochem. Syst. Ecol. 12, 63–84 (1984).CAS 
Article 

Google Scholar 
Djerassi, C. & Lam, W. K. Sponge phospholipids. Acc. Chem. Res. 24, 69–75 (1991).CAS 
Article 

Google Scholar 
Thiel, V. et al. A chemical view of the most ancient metazoa – Biomarker chemotaxonomy of hexactinellid sponges. Naturwissenschaften 89, 60–66 (2002).CAS 
Article 

Google Scholar 
Velosaotsy, N. et al. Phospholipid distribution and phospholipid fatty acids in four Saudi red sea sponges. Boll. Mus. Ist. Biol. Univ. Genova 68, 639–645 (2004).
Google Scholar 
Rod’kina, S. A. Fatty acids and other lipids of marine sponges. Russ. J. Mar. Biol. 31, S49–S60 (2005).Article 

Google Scholar 
Blumenberg, M. & Michaelis, W. High occurrences of brominated lipid fatty acids in boreal sponges of the order Halichondrida. Mar. Biol. 150, 1153–1160 (2007).CAS 
Article 

Google Scholar 
Genin, E. et al. New trends in phospholipid class composition of marine sponges. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 150, 427–431 (2008).Article 

Google Scholar 
Müller, W. et al. Role of the aggregation factor in the regulation of phosphoinositide metabolism in sponges. Possible consequences on calcium efflux and on mitogenesis. J. Biol. Chem. 262, 9850–9858 (1987).Article 

Google Scholar 
Weissmann, G., Riesen, W., Davidson, S. & Waite, M. Stimulus-response coupling in marine sponge cell aggregation: Lipid metabolism and the function of exogenously added arachidonic and docosahexaenoic acids. Biochim. Biophys. Acta 960, 351–364 (1988).CAS 
Article 

Google Scholar 
Zivanovic, A., Pastro, N. J., Fromont, J., Thomson, M. & Skropeta, D. Kinase inhibitory, haemolytic and cytotoxic activity of three deep-water sponges from North Western Australia and their fatty acid composition. Nat. Prod. Commun. 6, 1921–1924 (2011).CAS 

Google Scholar 
Shaaban, M., Abd-Alla, H. I., Hassan, A. Z., Aly, H. F. & Ghani, M. A. Chemical characterization, antioxidant and inhibitory effects of some marine sponges against carbohydrate metabolizing enzymes. Org. Med. Chem. Lett. 2, 30 (2012).Article 

Google Scholar 
Botić, T. et al. Fatty acid composition and antioxidant activity of Antarctic marine sponges of the genus Latrunculia. Polar Biol. 38, 1605–1612 (2015).Article 

Google Scholar 
Bennett, H., Bell, J. J., Davy, S. K., Webster, N. S. & Francis, D. S. Elucidating the sponge stress response; lipids and fatty acids can facilitate survival under future climate scenarios. Glob. Chang. Biol. 24, 3130–3144 (2018).Article 

Google Scholar 
Carballeira, N. M. New advances in fatty acids as antimalarial, antimycobacterial and antifungal agents. Prog. Lipid Res. 47, 50–61 (2008).CAS 
Article 

Google Scholar 
Kikuchi, H. et al. Marine natural products. X. Pharmacologically active glycolipids from the Okinawan marine sponge Phyllospongia foliascens (Pallas). Chem. Pharm. Bull. (Tokyo) 30, 3544–3547 (1982).CAS 
Article 

Google Scholar 
Natori, T., Morita, M., Akimoto, K. & Koezuka, Y. Agelasphins, novel antitumor and immunostimulatory cerebrosides from the marine sponge Agelas mauritianus. Tetrahedron 50, 2771–2784 (1994).CAS 
Article 

Google Scholar 
Costantino, V., Fattorusso, E., Mangoni, A., Di Rosa, M. & Ianaro, A. Glycolipids from Sponges. 6. Plakoside A and B, two unique prenylated glycosphingolipids with Immunosuppressive activity from the marine sponge Plakortis simplex. J. Am. Chem. Soc. 119, 12465–12470 (1997).CAS 
Article 

Google Scholar 
Costantino, V., Fattorusso, E., Imperatore, C. & Mangoni, A. Glycolipids from sponges. 11. Isocrasserides, novel glycolipids with a five-membered cyclitol widely distributed in marine sponges. J. Nat. Prod. 65, 883–886 (2002).CAS 
Article 

Google Scholar 
Maldonado, M. & Riesgo, A. Reproduction in Porifera: a synoptic overview. Treballs la Soc. Catalana Biol. 59, 29–49 (2008).
Google Scholar 
Sciscioli, M., Lepore, E., Scalera-Liaci, L. & Gherardi, M. Indagine ultrastrutturale sugli ovociti di Erylus discophorus (Schmidt) (Porifera, Tetractinellida). Oebalia 15, 939–941 (1989).
Google Scholar 
Sciscioli, M., Liaci, L. S., Lepore, E., Gherardi, M. & Simpson, T. L. Ultrastructural study of the mature egg of the marine sponge Stelletta grubii (porifera demospongiae). Mol. Reprod. Dev. 28, 346–350 (1991).CAS 
Article 

Google Scholar 
Riesgo, A. et al. Some like it fat: comparative ultrastructure of the embryo in two demosponges of the genus Mycale (order Poecilosclerida) from Antarctica and the Caribbean. PLoS ONE 10, e0118805 (2015).Article 

Google Scholar 
Watanabe, Y. The development of two species of Tetilla (Demosponge). NSR. O. U. 29, 71–106 (1978).
Google Scholar 
Gaino, E. & Sarà, M. An ultrastructural comparative study of the eggs of two species of Tethya (Porifera, Demospongiae). Invertebr. Reprod. Dev. 26, 99–106 (1994).Article 

Google Scholar 
Maldonado, M. & Riesgo, A. Gametogenesis, embryogenesis, and larval features of the oviparous sponge Petrosia ficiformis (Haplosclerida, Demospongiae). Mar. Biol. 156, 2181–2197 (2009).Article 

Google Scholar 
Lanna, E. & Klautau, M. Oogenesis and spermatogenesis in Paraleucilla magna (Porifera, Calcarea). Zoomorphology 129, 249–261 (2010).Article 

Google Scholar 
Koutsouveli, V. et al. Insights into the reproduction of some Antarctic dendroceratid, poecilosclerid, and haplosclerid demosponges. PLoS ONE 13, 1–24 (2018).Article 

Google Scholar 
Fell, P. E. The involvement of nurse cells in oogenesis and embryonic development in the marine sponge, Haliclona ecbasis. J. Morphol. 127, 133–149 (1969).Article 

Google Scholar 
Simpson, T. The Cell Biology of Sponges (Springer, 1984).Book 

Google Scholar 
Bellairs, R. The structure of the yolk of the hen’s egg as studied by electron microscopy : i. The yolk of the unincubated egg. J. Biophys. Biochem. Cytol. 11, 207–225 (1961).CAS 
Article 

Google Scholar 
Ereskovsky, A. The Comparative Embryology of Sponges (Springer, 2010).Book 

Google Scholar 
Sarà, A., Cerrano, C. & Sarà, M. Viviparous development in the Antarctic sponge Stylocordyla borealis Loven, 1868. Polar Biol. 25, 425–431 (2002).Article 

Google Scholar 
Busch, K. et al. Chloroflexi dominate the deep-sea golf ball sponges Craniella zetlandica and Craniella infrequens throughout different life stages. Front. Mar. Sci. 7, 674 (2020).Article 

Google Scholar 
Koopmans, M. et al. Seasonal variation of fatty acids and stable carbon isotopes in sponges as indicators for nutrition: Biomarkers in sponges identified. Mar. Biotechnol. 17, 43–54 (2015).CAS 
Article 

Google Scholar 
Lüskow, F. et al. Seasonality in lipid content of the demosponges Halichondria panicea and H. bowerbanki at two study sites in temperate Danish waters. Front. Mar. Sci. 6, 1–7 (2019).Article 

Google Scholar 
Reiswig, H. Population dynamics of three Jamaican demospongiae. Bull. Mar. Sci. 23, 191–226 (1973).
Google Scholar 
Elvin, D. W. Seasonal growth and reproduction of an intertidal sponge, Haliclona permollis (Bowerbank). Univ. Chicago Press 151, 108–125 (1976).
Google Scholar 
Elvin, D. W. The relationship of seasonal changes in the biochemical components to the reproductive behavior of the intertidal sponge, Haliclona permollis. Biol Bull. 156, 47–61 (1979).CAS 
Article 

Google Scholar 
Barthel, D. On the ecophysiology of the sponge Halichondria panicea in Kiel Bight. I. Substrate specificity, growth and reproduction. Mar. Ecol. Prog. Ser. 32, 291–298 (1986).Article 

Google Scholar 
Ivanisevic, J., Pérez, T., Ereskovsky, A. V., Barnathan, G. & Thomas, O. P. Lysophospholipids in the Mediterranean sponge Oscarella tuberculata: Seasonal variability and putative biological role. J. Chem. Ecol. 37, 537 (2011).CAS 
Article 

Google Scholar 
Klitgaard, A. B. The fauna associated with outer shelf and upper slope sponges (Porifera, demospongiae) at the Faroe islands, North-eastern Atlantic. Sarsia 80, 1–22 (1995).Article 

Google Scholar 
Klitgaard, A. B. & Tendal, O. Distribution and species composition of mass occurrences of large-sized sponges in the northeast Atlantic. Prog. Oceanogr. 61, 57–98 (2004).Article 

Google Scholar 
Kutti, T., Bannister, R. J. & Fosså, J. H. Community structure and ecological function of deep-water sponge grounds in the Traenadypet MPA—Northern Norwegian continental shelf. Cont. Shelf Res. 69, 21–30 (2013).Article 

Google Scholar 
Pile, A. & Young, C. The natural diet of a hexactinellid sponge: Benthic–pelagic coupling in a deep-sea microbial food web. Deep Sea Res. Part I Oceanogr. Res. Pap. 53, 1148–1156 (2006).Article 

Google Scholar 
Yahel, G., Whitney, F., Reiswig, H. M., Eerkes-Medrano, D. I. & Leys, S. P. In situ feeding and metabolism of glass sponges (Hexactinellida, Porifera) studied in a deep temperate fjord with a remotely operated submersible. Limnol. Oceanogr. 52, 428–440 (2007).CAS 
Article 

Google Scholar 
Hoffmann, F. et al. Complex nitrogen cycling in the sponge Geodia barretti. Environ. Microbiol. 11, 2228–2243 (2009).CAS 
Article 

Google Scholar 
Cathalot, C. et al. Cold-water coral reefs and adjacent sponge grounds: hotspots of benthic respiration and organic carbon cycling in the deep sea. Front. Mar. Sci. 2, 1–12 (2015).Article 

Google Scholar 
Kahn, A., Yahel, G., Chu, J., Tunnicliffe, V. & Leys, S. Benthic grazing and carbon sequestration by deep-water glass sponge reefs. Limnol. Oceanogr. 60, 78–88 (2015).Article 

Google Scholar 
Rooks, C. et al. Deep-sea sponge grounds as nutrient sinks: denitrification is common in boreo-Arctic sponges. Biogeosciences 17, 1231–1245 (2020).CAS 
Article 

Google Scholar 
Koutsouveli, V., Cárdenas, P., Conejero, M., Rapp, H. T. & Riesgo, A. Reproductive biology of Geodia species (Porifera, Tetractinellida) from Boreo-Arctic North-Atlantic Deep-Sea Sponge Grounds. Front. Mar. Sci. 7, 1–22 (2020).Article 

Google Scholar 
Reynolds, E. S. The use of lead citrate at high PH as an electron-opaque stain in electron microscopy. J. Cell Biol. 17, 208–212 (1963).CAS 
Article 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
Article 

Google Scholar 
Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).CAS 
Article 

Google Scholar 
Balgoma, D. et al. Anabolic androgenic steroids exert a selective remodeling of the plasma lipidome that mirrors the decrease of the de novo lipogenesis in the liver. Metabolomics 16, 12 (2020).CAS 
Article 

Google Scholar 
Kolmert, J. et al. Prominent release of lipoxygenase generated mediators in a murine house dust mite-induced asthma model. Prostaglandins Other Lipid Mediat. 137, 20–29 (2018).CAS 
Article 

Google Scholar 
Balgoma, D. et al. Linoleic acid-derived lipid mediators increase in a female-dominated subphenotype of COPD. Eur. Respir. J. 47, 1645–1656 (2016).CAS 
Article 

Google Scholar 
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).CAS 
Article 

Google Scholar 
Tautenhahn, R., Böttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinform. 9, 504 (2008).Article 

Google Scholar 
Fahy, E., Sud, M., Cotter, D. & Subramaniam, S. LIPID MAPS online tools for lipid research. Nucleic Acids Res. 35, W606–W612 (2007).Article 

Google Scholar 
Böcker, S., Letzel, M. C., Lipták, Z. & Pervukhin, A. SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics 25, 218–224 (2008).Article 

Google Scholar 
Koutsouveli, V. et al. The molecular machinery of gametogenesis in Geodia demosponges (Porifera): Evolutionary origins of a conserved toolkit across animals. Mol. Biol. Evol. 37, 3485–3506 (2020).CAS 
Article 

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

Google Scholar 
Grabherr, M. G. et al. Trinity: reconstructing a full-length transcriptome without a genome assembly from RNA-Seq data. Nat. Biotechnol. 29, 644–652 (2011).CAS 
Article 

Google Scholar 
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
Article 

Google Scholar 
Li, B. & Dewey, C. N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).CAS 
Article 

Google Scholar 
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).Article 

Google Scholar 
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).CAS 
Article 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).CAS 
Article 

Google Scholar 
Boeckmann, B. et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003).CAS 
Article 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59 (2014).Article 

Google Scholar 
Conesa, A. et al. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).CAS 
Article 

Google Scholar 
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
Article 

Google Scholar 
Busch, K. et al. Population connectivity of fan-shaped sponge holobionts in the deep Cantabrian Sea. Deep Sea Res. Part I Oceanogr. Res. Pap. 167, 103427 (2020).Article 

Google Scholar 
Southwood, T. R. Habitat, the templet for ecological strategies. J. Anim. Ecol. 46, 336–365 (1977).Article 

Google Scholar 
Clarke, A. A reappraisal of the concept of metabolic cold adaptation in polar marine invertebrates. Biol. J. Linn. Soc. 14, 77–92 (1980).Article 

Google Scholar 
Witte, U. Seasonal reproduction in deep-sea sponges—Triggered by vertical particle flux?. Mar. Biol. 124, 571–581 (1996).Article 

Google Scholar 
Spetland, F., Rapp, H. T., Hoffmann, F. & Tendal, O. S. Sexual reproduction of Geodia barretti Bowerbank, 1858 (Porifera, Astrophorida) in two Scandinavian fjords. In Porifera Research: Biodiversity, Innovation, Sustainability Vol. 1858 (eds Custódio, M. et al.) 613–620 (Série Livros. Museu Nacional, 2007).
Google Scholar 
Wassmann, P. Dynamics of primary production and sedimentation in shallow fjords and polls of western Norway. Oceanogr. Mar. Biol. Annu. Rev. 29, 87–154 (1991).
Google Scholar 
Wassmann, P., Svendsen, H., Keck, A. & Reigstad, M. Selected aspects of the physical oceanography and particle fluxes in fjords of northern Norway. J. Mar. Syst. 8, 53–71 (1996).Article 

Google Scholar 
Bamstedt, U. Life cycle, seasonal vertical distribution and feeding of Calanus finmarchicus in Skagerrak coastal water. Mar. Biol. 137, 279–289 (2000).Article 

Google Scholar 
Eckelbarger, K. J. & Watling, L. Role of phylogenetic constraints in determining reproductive patterns in deep-sea invertebrates. Invertebr. Biol. 114, 256–269 (1995).Article 

Google Scholar 
Riesgo, A. & Maldonado, M. Ultrastructure of oogenesis of two oviparous demosponges: Axinella damicornis and Raspaciona aculeata (Porifera). Tissue Cell 41, 51–65 (2009).Article 

Google Scholar 
Whiteley, N. M., Taylor, E. W. & el Haj, A. J. A comparison of the metabolic cost of protein synthesis in stenothermal and eurythermal isopod crustaceans. Am. J. Physiol. 271, R1295–R1303 (1996).CAS 
Article 

Google Scholar 
Pace, D. A. & Manahan, D. T. Cost of protein synthesis and energy allocation during development of Antarctic sea urchin embryos and larvae. Biol. Bull. 212, 115–129 (2007).CAS 
Article 

Google Scholar 
Sciscioli, M., Lepore, E., Gherardi, M. & Liaci, L. S. Transfer of symbiotic bacteria in the mature oocyte of Geodia cydonium (Porifera, Demosponsgiae): An ultrastructural study. Cah. Biol. Mar. 35, 471–478 (1994).
Google Scholar 
McWilliams, S. R., Guglielmo, C., Pierce, B. & Klaassen, M. Flying, fasting, and feeding in birds during migration: A nutritional and physiological ecology perspective. J. Avian Biol. 35, 377–393 (2004).Article 

Google Scholar 
Derickson, W. K. Lipid storage and utilization in reptiles. Am. Zool. 16, 711–723 (1976).CAS 
Article 

Google Scholar 
Fraser, A. J. Triacylglycerol content as a condition index for fish, bivalve, and crustacean larvae. Can. J. Fish. Aquat. Sci. 46, 1868–1873 (1989).CAS 
Article 

Google Scholar 
Bonnet, X., Naulleau, G. & Mauget, R. The influence of body condition on 17-beta estradiol levels in relation to vitellogenesis in female Vipera aspis (Reptilia, Viperidae). Gen. Comp. Endocrinol. 93, 424–437 (1994).CAS 
Article 

Google Scholar 
Duggan, A. et al. Seasonal variation in plasma lipids, lipoproteins, apolipoprotein A-I and vitellogenin in the freshwater turtle, Chrysemys picta. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 130, 253–269 (2001).CAS 
Article 

Google Scholar 
Lance, V. A., Place, A. R., Grumbles, J. S. & Rostal, D. C. Variation in plasma lipids during the reproductive cycle of male and female desert tortoises, Gopherus agassizii. J. Exp. Zool. 293, 703–711 (2002).CAS 
Article 

Google Scholar 
Kawazu, I. et al. Signals of vitellogenesis and estrus in female hawksbill turtles. Zoolog. Sci. 32, 114–118 (2015).Article 

Google Scholar 
Teshima, S. & Kanazawa, A. Variation in lipid compositions during the ovarian maturation of the prawn. Nippon Suisan Gakkaishi 49, 957–962 (1983).CAS 
Article 

Google Scholar 
Clarke, A., Brown, J. H. & Holmes, L. J. The biochemical composition of eggs from Macrobrachium rosenbergii in relation to embryonic development. Comp. Biochem. Physiol. Part B Comp. Biochem. 96, 505–511 (1990).Article 

Google Scholar 
Allen, W. Amino acid and fatty acid composition of tissues of the dungeness crab (Cancer magister). J. Fish. Res. Board Canada 28, 1191–1195 (1971).CAS 
Article 

Google Scholar 
Rosa, R. & Nunes, M. L. Tissue biochemical composition in relation to the reproductive cycle of deep-sea decapod Aristeus antennatus in the Portuguese south coast. J. Mar. Biol. Assoc. U. K. 83, 963–970 (2003).CAS 
Article 

Google Scholar 
Balgoma, D., Pettersson, C. & Hedeland, M. Common fatty markers in diseases with dysregulated lipogenesis. Trends Endocrinol. Metab. 30, 283–285 (2019).CAS 
Article 

Google Scholar 
Kent, C. Eukaryotic phospholipid biosynthesis. Annu. Rev. Biochem. 64, 315–343 (1995).CAS 
Article 

Google Scholar 
Coleman, R. A. & Lee, D. P. Enzymes of triacylglycerol synthesis and their regulation. Prog. Lipid Res. 43, 134–176 (2004).CAS 
Article 

Google Scholar 
Bell, R. M. & Coleman, R. A. Enzymes of glycerolipid synthesis in eukaryotes. Annu. Rev. Biochem. 49, 459–487 (1980).CAS 
Article 

Google Scholar 
Mathews, C., van Holde, K., Appling, D. & Anthony-Cahill, S. Biochemistry (Pearson, 2019).
Google Scholar 
Gavaud, J. Histochemical identification of ovarian lipids during vitellogenesis in the lizard Lacerta vivipara. Can. J. Zool. 69, 1389–1392 (1991).Article 

Google Scholar 
Chapman, M. J. Animal lipoproteins: Chemistry, structure, and comparative aspects. J. Lipid Res. 21, 789–853 (1980).CAS 
Article 

Google Scholar 
Lebouvier, M., Miramón-Puértolas, P. & Steinmetz, P.R. Evolutionary conserved aspects of animal nutrient uptake and transport in sea anemone vitellogenesis. bioRxiv (2022).Dolphin, P. J., Ansari, A. Q., Lazier, C. B., Munday, K. A. & Akhtar, M. Studies on the induction and biosynthesis of vitellogenin, an oestrogen-induced glycolipophosphoprotein. Biochem. J. 124, 751–758 (1971).CAS 
Article 

Google Scholar 
Riesgo, A., Farrar, N., Windsor, P. J., Giribet, G. & Leys, S. P. The analysis of eight transcriptomes from all poriferan classes reveals surprising genetic complexity in sponges. Mol. Biol. Evol. 31, 1102–1120 (2014).CAS 
Article 

Google Scholar 
Wanders, R. J. A. Peroxisomes, lipid metabolism, and peroxisomal disorders. Mol. Genet. Metab. 83, 16–27 (2004).CAS 
Article 

Google Scholar 
Wanders, R. J. A., Waterham, H. R. & Ferdinandusse, S. Metabolic interplay between peroxisomes and other subcellular organelles including mitochondria and the endoplasmic reticulum. Front. Cell Dev. Biol. 3, 83 (2016).Article 

Google Scholar 
Talley, J. & Mohiuddin, S. Biochemstry, Fatty Acid Oxidation (StatPearls, 2020).
Google Scholar 
Reiswig, H. M. Particle feeding in natural populations of three marine demosponges. Biol. Bull. 141, 568–591 (1971).Article 

Google Scholar 
Sugimoto, Y., Inazumi, T. & Tsuchiya, S. Roles of prostaglandin receptors in female reproduction. J. Biochem. 157, 73–80 (2015).CAS 
Article 

Google Scholar 
Niringiyumukiza, J. D., Cai, H. & Xiang, W. Prostaglandin E2 involvement in mammalian female fertility: ovulation, fertilization, embryo development and early implantation. Reprod. Biol. Endocrinol. 16, 43 (2018).Article 

Google Scholar 
Kaczynski, P., Baryla, M., Goryszewska, E., Bauersachs, S. & Waclawik, A. Prostaglandin F2α promotes embryo implantation and development in the pig. Reproduction 156, 405–419 (2018).CAS 

Google Scholar 
De Petrocellis, L. & Di Marzo, V. Aquatic invertebrates open up new perspectives in eicosanoid research: Biosynthesis and bioactivity. Prostaglandins Leukot. Essent. Fat. Acids 51, 215–229 (1994).Article 

Google Scholar 
Destephano, D. B. & Brady, U. E. Prostaglandin and prostaglandin synthetase in the cricket, Acheta domesticus. J. Insect Physiol. 23, 905–911 (1977).CAS 
Article 

Google Scholar 
Rich, A. M. et al. Calcium dependent aggregation of marine sponge cells is provoked by leukotriene B4 and inhibited by inhibitors of arachidonic acid oxidation. Biochem. Biophys. Res. Commun. 121, 863–870 (1984).CAS 
Article 

Google Scholar 
Gramzow, M. et al. Role of phospholipase A2 in the stimulation of sponge cell proliferation by homologous lectin. Cell 59, 939–948 (1989).CAS 
Article 

Google Scholar 
Nomura, T. & Ogata, H. Distribution of prostagladins in the animal kingdom. Biochim. Biophys. Acta 431, 127–131 (1976).CAS 
Article 

Google Scholar  More

Fischer, R. et al. Accelerated forest fragmentation leads to critical increase in tropical forest edge area. Sci. Adv. 7, eabg7012 (2021).ADS 
CAS 
Article 

Google Scholar 
Newmark, W. D. & McNeally, P. B. Impact of habitat fragmentation on the spatial structure of the Eastern Arc forests in East Africa: Implications for biodiversity conservation. Biodivers. Conserv. 27, 1387–1402 (2018).Article 

Google Scholar 
Hall, J., Burgess, N. D., Lovett, J., Mbilinyi, B. & Gereau, R. E. Conservation implications of deforestation across an elevational gradient in the Eastern Arc Mountains, Tanzania. Biol. Conserv. 142, 2510–2521 (2009).Article 

Google Scholar 
Kuussaari, M. et al. Extinction debt: A challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 

Google Scholar 
Gibson, L. et al. Near-complete extinction of native small mammal fauna 25 years after forest fragmentation. Science 341, 1508–1510 (2013).ADS 
CAS 
Article 

Google Scholar 
Burgess, N. D. et al. The biological importance of the Eastern Arc Mountains of Tanzania and Kenya. Biol. Conserv. 134, 209–231 (2007).Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
Article 

Google Scholar 
Oates, J. F. et al. A new species of tree hyrax (Procaviidae: Dendrohyrax) from West Africa and the significance of the Niger-Volta interfluvium in mammalian biogeography. Zool. J. Linn. Soc. 194, 527–552 (2022).Article 

Google Scholar 
Bloomer, P. Extant hyrax diversity is vastly underestimated. Afrotherian. Conserv. 7, 11–16 (2009).
Google Scholar 
Roberts, D., Topp-Jørgensen, E. & Moyer, D. C. Dendrohyrax validus Eastern Tree Hyrax. In Mammals of Africa Vol. I (eds Kingdon, J. et al.) 158–161 (Bloomsbury, 2013).
Google Scholar 
Hoeck, H. Some thoughts on the distribution of the tree hyraxes (genus Dendrohyrax) in northern Tanzania. Afrotherian Conserv. 13, 47–49 (2017).
Google Scholar 
Rosti, H., Pihlström, H., Bearder, S., Pellikka, P. & Rikkinen, J. Vocalization analyses of nocturnal arboreal mammals of the Taita Hills, Kenya. Diversity 12, 473 (2020).Article 

Google Scholar 
Roberts, D. Geographic variation in the loud calls of tree hyrax – Dendrohyrax validus (True 1890) In the Eastern Arc Mountains, East Africa: taxonomic and conservation implications. (MSc thesis, University of Reading, 2001).True, F. W. Description of two new species of mammals from Mt. Kilima-Njaro, East Africa. Proc. US Nat. Mus. 13, 227–229 (1890).Article 

Google Scholar 
True, F. W. An annotated catalogue of the mammals collected by Dr. W. L. Abbott in the Kilma-Njaro region, East Africa. Proc. U. S. Nat. Mus. 15, 445–480 (1892).Article 

Google Scholar 
Kundaeli, J. N. Distribution of tree hyrax (Dendrohyrax validus validus True) on Mt Kilimanjaro, Tanzania. Afr. J. Ecol. 14, 253–264 (1976).Article 

Google Scholar 
Gaylard, A. & Kerley, G. I. H. Diet of tree hyraxes Dendrohyrax arboreus (Hyracoidea: Procaviidae) in the Eastern Cape, South Africa. J. Mammal. 78, 213–221 (1997).Article 

Google Scholar 
Milner, J. Relationships between the forest dwelling people of south-west Mau and tree hyrax, Dendrohyrax arboreus. J. East Afr. Nat. Hist. 83, 17–29 (1994).Article 

Google Scholar 
Milner, J. M. & Harris, S. Habitat use and ranging behaviour of tree hyrax, Dendrohyrax arboreus, in the Virunga Volcanoes, Rwanda. Afr. J. Ecol. 37, 281–294 (1999).Article 

Google Scholar 
Gaylard, A. & Kerley, G. I. H. Habitat assessment for a rare, arboreal forest mammal, the tree hyrax (Dendrohyrax arboreus). Afr. J. Ecol. 39, 205–212 (2001).Article 

Google Scholar 
Djossa, B., Zachee, B. & Sinzin, B. Activity patterns and habitat use of the western tree hyrax (Dendrohyrax dorsalis), within forest patches and implications for conservation. Ecotropica 18, 65–72 (2012).
Google Scholar 
Opperman, E. J., Cherry, M. I. & Makunga, N. P. Community harvesting of trees used as dens and for food by the tree hyrax (Dendrohyrax arboreus) in the Pirie forest, South Africa. Koedoe 60, a1481 (2018).
Cordeiro, N. J. et al. Notes on the ecology and status of some forest mammals in four Eastern Arc Mountains, Tanzania. J. East Afr. Nat. Hist. 94, 175–189 (2005).Article 

Google Scholar 
Koren, L. Vocalization as an indicator of individual quality in the rock hyrax. (PhD thesis, Tel-Aviv University, 2006).Koren, L., Mokady, O. & Geffen, E. Social status and cortisol levels in singing rock hyraxes. Horm. Behav. 54, 212–216 (2008).CAS 
Article 

Google Scholar 
Koren, L. & Geffen, E. Complex call in male rock hyrax (Procavia capensis): A multi-information distributing channel. Behav. Ecol. Sociobiol. 63, 581–590 (2009).Article 

Google Scholar 
Lawes, M. J., Mealin, P. E. & Piper, S. E. Patch occupancy and potential metapopulation dynamics of three forest mammals in fragmented Afromontane forest in South Africa. Conserv. Biol. 14, 1088–1098 (2000).Article 

Google Scholar 
Topp-Jørgensen, J. E., Marshal, A. R., Brink, H. & Pedersen, U. B. Quantifying the response of tree hyraxes (Dendrohyrax validus) to human disturbance in the Udzungwa Mountains, Tanzania. Trop. Conserv. Sci. 1, 63–74 (2008).Article 

Google Scholar 
Hill, A. P. et al. AudioMoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods Ecol. Evol. 9, 1199–1211 (2018).Article 

Google Scholar 
Marques, T. A. et al. Estimating animal population density using passive acoustics. Biol. Rev. 88, 287–309 (2013).Article 

Google Scholar 
Pérez-Granados, C. & Traba, J. Estimating bird density using passive acoustic monitoring: A review of methods and suggestions for further research. Ibis 163, 765–783 (2021).Article 

Google Scholar 
Campos-Cerqueira, M. & Aide, T. M. Improving distribution data of threatened species by combining acoustic monitoring and occupancy modelling. Methods Ecol. Evol. 7, 1340–1348 (2016).Article 

Google Scholar 
McLean, K. A. et al. Movement patterns of three arboreal primates in a Neotropical moist forest explained by LiDAR-estimated canopy structure. Landsc. Ecol. 31, 1849–1862 (2016).Article 

Google Scholar 
Davies, A. B., Ancrenaz, M., Oram, F. & Asner, G. P. Canopy structure drives orangutan habitat selection in disturbed Bornean forests. Proc. Natl. Acad. Sci. USA 114, 8307–8312 (2017).CAS 
Article 

Google Scholar 
Singh, M., Cheyne, S. M. & Ehlers Smith, D. A. How conspecific primates use their habitats: Surviving in an anthropogenically-disturbed forest in Central Kalimantan, Indonesia. Ecol. Indic. 87, 167–177 (2018).Article 

Google Scholar 
Simonson, W. D., Allen, H. D. & Coomes, D. A. Applications of airborne lidar for the assessment of animal species diversity. Methods Ecol. Evol. 5, 719–729 (2014).Article 

Google Scholar 
Aerts, R. et al. Woody plant communities of isolated Afromontane cloud forests in Taita Hills, Kenya. Plant Ecol. 212, 639–649 (2011).Article 

Google Scholar 
Lovett, J. C., Wasser, S. K., Cambridge University Press. Biogeography and Ecology of the Rain Forests of Eastern Africa (Cambridge University Press, 2008).
Google Scholar 
Pellikka, P. K. E., Lötjönen, M., Siljander, M. & Lens, L. Airborne remote sensing of spatiotemporal change (1955–2004) in indigenous and exotic forest cover in the Taita Hills, Kenya. Int. J. Appl. Earth Obs. Geoinf. 11, 221–232 (2009).ADS 
Article 

Google Scholar 
Rovero, F. et al. Targeted vertebrate surveys enhance the faunal importance and improve explanatory models within the Eastern Arc Mountains of Kenya and Tanzania. Diversity Distrib. 20, 1438–1449 (2014).Article 

Google Scholar 
Rosti, H., Rikkinen, J., Pellikka, P., Bearder, S. & Mwamodenyi, J. M. Taita Mountain dwarf galago is extant in the Taita Hills of Kenya. Oryx 54, 152–153 (2020).Article 

Google Scholar 
Pihlström, H., Rosti, H., Lombo, B. & Pellikka, P. Domestic dog predation on white-tailed small-eared galago (Otolemur garnettii lasiotis) in the Taita Hills, Kenya. Afr. Primates 15, 31–38 (2021).
Google Scholar 
Etana, B. et al. Traditional shade coffee forest systems act as refuges for medium- and large-sized mammals as natural forest dwindles in Ethiopia. Biol. Conserv. 260, 109219 (2021).Article 

Google Scholar 
Hoeck, H., Rovero, F., Cordeiro, N., Butynski, T., Perkin, A. & Jones, T. Dendrohyrax validus. The IUCN Red List of Threatened Species (2015: e.T136599A21288090).Himberg, N. Traditionally protected forests’ role within transforming natural resource management regimes in Taita Hills, Kenya. (PhD thesis, University of Helsinki, 2011).Thijs, K. W., Roelen, I. & Musila, W. M. Field guide to the woody plants of Taita Hills, Kenya. J. East Afr. Nat. Hist. 102, 1–272 (2014).Article 

Google Scholar 
Yéboué, K. Y. et al. Genetic typing and in silico assignment of smoked and fresh bushmeat sold on markets and restaurants in west-central Côte d’Ivoire. Int. J. Genet. Mol. Biol. 13, 1–8 (2021).Article 

Google Scholar 
Brown, K. J. & Downs, C. T. Seasonal behavioural patterns of free-living rock hyrax (Procavia capensis). J. Zool. 265, 311–326 (2005).Article 

Google Scholar 
Brown, K. J. & Downs, C. T. Seasonal patterns in body temperature of free-living rock hyrax (Procavia capensis). Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 143, 42–49 (2006).Article 

Google Scholar 
Ilany, A., Barocas, A., Kam, M., Ilany, T. & Geffen, E. The energy cost of singing in wild rock hyrax males: Evidence for an index signal. Anim. Behav. 85, 995–1001 (2013).Article 

Google Scholar 
Demartsev, V. et al. Male hyraxes increase song complexity and duration in the presence of alert individuals. Behav. Ecol. 25, 1451–1458 (2014).Article 

Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).ADS 
CAS 
Article 

Google Scholar 
Adhikari, H. et al. Determinants of aboveground biomass across an Afromontane landscape mosaic in Kenya. Remote Sens. 9, 827 (2017).ADS 
Article 

Google Scholar 
Heiskanen, J., Korhonen, L., Hietanen, J. & Pellikka, P. K. E. Use of airborne lidar for estimating canopy gap fraction and leaf area index of tropical montane forests. Int. J. Remote Sens. 36, 2569–2583 (2015).Article 

Google Scholar 
Roussel, J.-R. et al. lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sens. Environ. 251, 112061 (2020).ADS 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378 (2017).Article 

Google Scholar 
Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P. & Makowski, D. Performance: An R package for assessment, comparison and testing of statistical models. JOSS 6, 3139 (2021).ADS 
Article 

Google Scholar 
Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).MATH 
Book 

Google Scholar 
Zuur, A. F., Savelʹev, A. A. & Ieno, E. N. Zero Inflated Models and Generalized Linear Mixed Models with R (Highland Statistics, 2012).
Google Scholar 
Campbell, H. The consequences of checking for zero-inflation and overdispersion in the analysis of count data. Methods Ecol. Evol. 12, 665–680 (2021).Article 

Google Scholar 
Zuur, A. F. & Ieno, E. N. A protocol for conducting and presenting results of regression-type analyses. Methods Ecol. Evol. 7, 636–645 (2016).Article 

Google Scholar 
Aho, K., Derryberry, D. & Peterson, T. Model selection for ecologists: The worldviews of AIC and BIC. Ecology 95, 631–636 (2014).Article 

Google Scholar  More

Rothstein, S. I. A model system for coevolution: Avian brood parasitism. Annu. Rev. Ecol. Syst. 21, 481–508 (1990).Article 

Google Scholar 
Feeney, W. E. et al. Brood parasitism and the evolution of cooperative breeding in birds. Science 342, 1506–1508 (2013).ADS 
CAS 
Article 

Google Scholar 
Brooke, M. de L. & Davies, N. B. Egg mimicry by cuckoos Cuculus canorus in relation to discrimination by hosts. Nature 335, 630–632 (1988).ADS 
Article 

Google Scholar 
Medina, I. & Langmore, N. E. The costs of avian brood parasitism explain variation in egg rejection behaviour in hosts. Biol. Let. 11, 20150296 (2015).Article 

Google Scholar 
Langmore, N. E., Hunt, S. & Kilner, R. M. Escalation of a coevolutionary arms race through host rejection of brood parasitic young. Nature 422, 157–160 (2003).ADS 
CAS 
Article 

Google Scholar 
Grim, T. Experimental evidence for chick discrimination without recognition in a brood parasite host. Proc. R. Soc. B: Biol. Sci. 274, 373–381 (2007).Article 

Google Scholar 
Sato, N. J., Tokue, K., Noske, R. A., Mikami, O. K. & Ueda, K. Evicting cuckoo nestlings from the nest: A new anti-parasitism behaviour. Biol. Let. 6, 67–69. https://doi.org/10.1098/rsbl.2009.0540 (2010).Article 

Google Scholar 
Davies, N. & Brooke, M. de L. Cuckoos versus reed warblers: Adaptations and counteradaptations. Anim. Behav. 36, 262–284 (1988).Article 

Google Scholar 
Langmore, N. E. et al. Visual mimicry of host nestlings by cuckoos. Proc. R. Soc. B: Biol. Sci. 278, 2455–2463 (2011).Article 

Google Scholar 
Noh, H.-J., Gloag, R. & Langmore, N. E. True recognition of nestlings by hosts selects for mimetic cuckoo chicks. Proc. R. Soc. B: Bio. Sci. 285, 20180726 (2018).Article 

Google Scholar 
Spottiswoode, C. N. & Stevens, M. Host-parasite arms races and rapid changes in bird egg appearance. Am. Nat. 179, 633–648. https://doi.org/10.1086/665031 (2012).Article 

Google Scholar 
Taylor, C. J. & Langmore, N. E. How do brood-parasitic cuckoos reconcile conflicting environmental and host selection pressures on egg size investment?. Anim. Behav. 168, 89–96. https://doi.org/10.1016/j.anbehav.2020.08.003 (2020).Article 

Google Scholar 
Langmore, N. E., Maurer, G., Adcock, G. J. & Kilner, R. M. Socially acquired host-specific mimicry and the evolution of host races in Horsfield’s bronze-cuckoo Chalcites basalis. Evolution 62, 1689–1699 (2008).Article 

Google Scholar 
Noh, H. J., Jacomb, F., Gloag, R. & Langmore, N. E. Frontline defences against cuckoo parasitism in the large-billed gerygones. Anim. Behav. 174, 51–61. https://doi.org/10.1016/j.anbehav.2021.01.021 (2021).Article 

Google Scholar 
Langmore, N. E. & Kilner, R. M. Why do Horsfield’s bronze-cuckoo Chalcites basalis eggs mimic those of their hosts?. Behav. Ecol. Sociobiol. 63, 1127–1131. https://doi.org/10.1007/s00265-009-0759-9 (2009).Article 

Google Scholar 
Spottiswoode, C. N. & Stevens, M. How to evade a coevolving brood parasite: Egg discrimination versus egg variability as host defences. Proc. R. Soc. B: Biol. Sci. 278, 3566–3573. https://doi.org/10.1098/rspb.2011.0401 (2011).Article 

Google Scholar 
Yang, C., Wang, L., Liang, W. & Møller, A. P. Egg recognition as antiparasitism defence in hosts does not select for laying of matching eggs in parasitic cuckoos. Anim. Behav. 122, 177–181. https://doi.org/10.1016/j.anbehav.2016.10.018 (2016).Article 

Google Scholar 
Stevens, M. Bird brood parasitism. Curr. Biol. 23, R909–R913. https://doi.org/10.1016/j.cub.2013.08.025 (2013).MathSciNet 
CAS 
Article 

Google Scholar 
Feeney, W. E., Troscianko, J., Langmore, N. E. & Spottiswoode, C. N. Evidence for aggressive mimicry in an adult brood parasitic bird, and generalized defences in its host. Proc. R. Soc. B: Biol. Sci. 282, 20150795 (2015).Article 

Google Scholar 
Davies, N. B. & Welbergen, J. A. Cuckoo–hawk mimicry? An experimental test. Proc. R. Soc. B: Biol. Sci. 275, 1817–1822 (2008).CAS 
Article 

Google Scholar 
Brooker, L. C. & Brooker, M. G. Why are cuckoos host specific?. Oikos 57, 301–309. https://doi.org/10.2307/3565958 (1990).Article 

Google Scholar 
Langmore, N. E., Stevens, M., Maurer, G. & Kilner, R. M. Are dark cuckoo eggs cryptic in host nests?. Anim. Behav. 78, 461–468 (2009).Article 

Google Scholar 
Lack, D. L. Ecological Adaptations for Breeding in Birds (Methuen & Co., Ltd., 1968).
Google Scholar 
Spaw, C. D. & Rohwer, S. A comparative study of eggshell thickness in cowbirds and other passerines. The Condor 89, 307–318. https://doi.org/10.2307/1368483 (1987).Article 

Google Scholar 
Igic, B. et al. Alternative mechanisms of increased eggshell hardness of avian brood parasites relative to host species. J. R. Soc. Interface 8, 1654–1664. https://doi.org/10.1098/rsif.2011.0207 (2011).Article 

Google Scholar 
Brooker, M. G. & Brooker, L. C. Eggshell strength in cuckoos and cowbirds. Ibis 133, 406–413. https://doi.org/10.1111/j.1474-919X.1991.tb04589.x (1991).Article 

Google Scholar 
Maurer, G. et al. First light for avian embryos: eggshell thickness and pigmentation mediate variation in development and UV exposure in wild bird eggs. Funct. Ecol. 29, 209–218 (2015).Article 

Google Scholar 
Amos, A. & Rahn, H. Pores in avian eggshells: Gas conductance, gas exchange and embryonic growth rate. Respir. Physiol. 61, 1–20 (1985).Article 

Google Scholar 
Ar, A., Rahn, H. & Paganelli, C. V. The avian egg: Mass and strength. Condor 81, 331–337 (1979).Article 

Google Scholar 
Rahn, H. & Ar, A. Gas-exchange of the avian egg: Time, structure, and function. Am. Zool. 20, 477–484 (1980).Article 

Google Scholar 
Swynnerton, C. Rejections by birds of eggs unlike their own: With remarks on some of the cuckoo problems. Ibis 60, 127–154 (1918).Article 

Google Scholar 
López, A. V., Fiorini, V. D., Ellison, K. & Peer, B. D. Thick eggshells of brood parasitic cowbirds protect their eggs and damage host eggs during laying. Behav. Ecol. 29, 965–973 (2018).Article 

Google Scholar 
Wyllie, I. The Cuckoo (Batsford, 1981).
Google Scholar 
Yang, C. et al. Keeping eggs warm: Thermal and developmental advantages for parasitic cuckoos of laying unusually thick-shelled eggs. Sci. Nat. 105, 10 (2018).Article 

Google Scholar 
Davies, N. B. Cuckoos Cowbirds and other Cheats (T & A D Poyser, 2000).
Google Scholar 
Spottiswoode, C. N. The evolution of host-specific variation in cuckoo eggshell strength. J. Evol. Biol. 23, 1792–1799. https://doi.org/10.1111/j.1420-9101.2010.02010.x (2010).CAS 
Article 

Google Scholar 
Langmore, N. E. et al. The evolution of egg rejection by cuckoo hosts in Australia and Europe. Behav. Ecol. 16, 686–692. https://doi.org/10.1093/beheco/ari041 (2005).Article 

Google Scholar 
Rohwer, S., Spaw, C. D. & Røskaft, E. Costs to northern orioles of puncture-ejecting parasitic cowbird eggs from their nests. The Auk 106, 734–738 (1989).
Google Scholar 
Brooker, M. G., Brooker, L. C. & Rowley, I. Egg deposition by the bronze-cuckoos Chrysococcyx basalis and Chrysococcyx lucidus. Emu 88, 107–109. https://doi.org/10.1071/Mu9880107 (1988).Article 

Google Scholar 
McClelland, S. C. et al. Embryo movement is more frequent in avian brood parasites than birds with parental reproductive strategies. Proc. R. Soc B-Biol. Sci. https://doi.org/10.1098/rspb.2021.1137 (2021).Article 

Google Scholar 
Gosler, A. G. & Wilkin, T. A. Eggshell speckling in a passerine bird reveals chronic long-term decline in soil calcium. Bird Study 64, 195–204. https://doi.org/10.1080/00063657.2017.1314448 (2017).Article 

Google Scholar 
Lundholm, C. E. Inhibition of prostaglandin synthesis in eggshell gland mucosa as a mechanism for P, P’-DDE-induced eggshell thinning in birds: A comparison of ducks and domestic-fowls. Comp. Biochem. Phys. C 106, 389–394. https://doi.org/10.1016/0742-8413(93)90151-A (1993).Article 

Google Scholar 
Bitman, J., Cecil, H. C. & Fries, G. F. DDT-Induced inhibition of avian shell gland carbonic anhydrase: A mechanism for thin eggshells. Science 168, 594–596. https://doi.org/10.1126/science.168.3931.594 (1970).ADS 
CAS 
Article 

Google Scholar 
Ratcliffe, D. A. Changes attributable to pesticides in egg breakage frequency and eggshell thickness in some British birds. J. Appl. Ecol. 7, 67-+. https://doi.org/10.2307/2401613 (1970).Article 

Google Scholar 
Bouwman, H., Govender, D., Underhill, L. & Polder, A. Chlorinated, brominated and fluorinated organic pollutants in African Penguin eggs: 30 years since the previous assessment. Chemosphere 126, 1–10. https://doi.org/10.1016/j.chemosphere.2014.12.071 (2015).ADS 
CAS 
Article 

Google Scholar 
Bleu, J., Agostini, S., Angelier, F. & Biard, C. Experimental increase in temperature affects eggshell thickness, and not egg mass, eggshell spottiness or egg composition in the great tit (Parus major). Gen. Comp. Endocr. 275, 73–81. https://doi.org/10.1016/j.ygcen.2019.02.004 (2019).CAS 
Article 

Google Scholar 
Picman, J. & Pribil, S. Is greater eggshell density an alternative mechanism by which parasitic cuckoos increase the strength of their eggs?. J. Ornithol. 138, 531–541. https://doi.org/10.1007/bf01651384 (1997).Article 

Google Scholar 
Lopez, A. V. et al. How to build a puncture- and breakage-resistant eggshell? Mechanical and structural analyses of avian brood parasites and their hosts. J. Exp. Biol. 224, jeb243016. https://doi.org/10.1242/jeb.243016 (2021).Article 

Google Scholar 
Soler, M., Rodriguez-Navarro, A. B., Perez-Contreras, T., Garcia-Ruiz, J. M. & Soler, J. J. Great spotted cuckoo eggshell microstructure characteristics can make eggs stronger. J. Avian Biol. 50, e02252. https://doi.org/10.1111/jav.02252 (2019).Article 

Google Scholar 
D’Alba, L. et al. Evolution of eggshell structure in relation to nesting ecology in non-avian reptiles. J. Morphol. 282, 1066–1079. https://doi.org/10.1002/jmor.21347 (2021).CAS 
Article 

Google Scholar 
Legendre, L. J. & Clarke, J. A. Shifts in eggshell thickness are related to changes in locomotor ecology in dinosaurs. Evolution 75, 1415–1430. https://doi.org/10.1111/evo.14245 (2021).Article 

Google Scholar 
Le Roy, N., Stapane, L., Gautron, J. & Hincke, M. T. Evolution of the avian eggshell biomineralization protein toolkit: New insights from multi-omics. Front. Genet. 12, 672433. https://doi.org/10.3389/fgene.2021.672433 (2021).CAS 
Article 

Google Scholar 
Medina, I. & Langmore, N. E. Batten down the thatches: Front-line defences in an apparently defenceless cuckoo host. Anim. Behav. 112, 195–201. https://doi.org/10.1016/j.anbehav.2015.12.006 (2016).Article 

Google Scholar 
Starling, M., Heinsohn, R., Cockburn, A. & Langmore, N. E. Cryptic gentes revealed in pallid cuckoos Cuculus pallidus using reflectance spectrophotometry. Proc. R. Soc. Lond. B 273, 1929–1934 (2006).CAS 

Google Scholar 
Abernathy, V. E., Troscianko, J. & Langmore, N. E. Egg mimicry by the Pacific koel: Mimicry of one host facilitates exploitation of other hosts with similar egg types. J. Avian Biol. 48, 1414–1424. https://doi.org/10.1111/jav.01530 (2017).Article 

Google Scholar 
Green, R. E. An evaluation of three indices of eggshell thickness. Ibis 142, 676–679. https://doi.org/10.1111/j.1474-919X.2000.tb04468.x (2000).Article 

Google Scholar 
Green, R. E. Long-term decline in the thickness of eggshells of thrushes, Turdus spp., in Britain. Proc. R. Soc. London. Ser. B: Biol. Sci. 265, 679–684. https://doi.org/10.1098/rspb.1998.0347 (1998).Article 

Google Scholar 
Igic, B. et al. Comparison of micrometer-and scanning electron microscope-based measurements of avian eggshell thickness. J. Field Ornithol. 81, 402–410 (2010).Article 

Google Scholar 
Maurer, G., Portugal, S. J. & Cassey, P. A comparison of indices and measured values of eggshell thickness of different shell regions using museum eggs of 230 European bird species. Ibis 154, 714–724 (2012).Article 

Google Scholar 
Becking, J. The ultrastructure of the avian eggshell. Ibis 117, 143–151 (1975).Article 

Google Scholar 
Birkhead, T. et al. New insights from old eggs–the shape and thickness of Great Auk Pinguinus impennis eggs. Ibis 162(4), 1345–1354 (2020).Article 

Google Scholar 
Riley, A., Sturrock, C., Mooney, S. & Luck, M. Quantification of eggshell microstructure using X-ray micro computed tomography. Br. Poult. Sci. 55, 311–320 (2014).CAS 
Article 

Google Scholar 
Kibala, L., Rozempolska-Rucinska, I., Kasperek, K., Zieba, G. & Lukaszewicz, M. Ultrasonic eggshell thickness measurement for selection of layers. Poult. Sci. 94, 2360–2363. https://doi.org/10.3382/ps/pev254 (2015).Article 

Google Scholar 
Khaliduzzaman, A. et al. A nondestructive eggshell thickness measurement technique using terahertz waves. Sci. Rep. 10, 1–5 (2020).Article 

Google Scholar 
Santolo, G. M. A new nondestructive method for measuring eggshell thickness using a non-ferrous material thickness gauge. Wilson J. Ornithol. 130, 502–509. https://doi.org/10.1676/17-035.1 (2018).Article 

Google Scholar 
Marini, M. A. et al. The five million bird eggs in the world’s museum collections are an invaluable and underused resource. Auk 137, ukaa036. https://doi.org/10.1093/auk/ukaa036 (2020).Article 

Google Scholar 
Brooker, M. G. & Brooker, L. C. Cuckoo hosts in Australia. Aust. Zool. Rev. 2, 1–67 (1989).
Google Scholar 
Higgins, P. J. Vol. Volume 4: Parrots to Dollarbird (Oxford University Press, 1999).
Google Scholar 
Higgins, P. J. & Peter, J. M. Vol. 6: Pardalotes to Shrike-Thrushes (Oxford University Press, 2002).
Google Scholar 
Higgins, P. J., Peter, J. M. & Cowling, S. J. Vol. 4: Parrots to Dollarbird (Oxford University Press, 2006).
Google Scholar 
Higgins, P. J., Peter, J. M. & Steele, W. K. Vol. 5: Tyrant-flycatchers to Chats (Oxford University Press, 2001).
Google Scholar 
Landstrom, M., Heinsohn, R. & Langmore, N. E. Clutch variation and egg rejection in three hosts of the pallid cuckoo Cuculus pallidus. Behaviour 147, 19–36. https://doi.org/10.1163/000579509X12483520922043 (2010).Article 

Google Scholar 
Abernathy, V. E., Johnson, L. E. & Langmore, N. E. An experimental test of defenses against avian brood parasitism in a recent host. Front. Ecol. Evol. 9, 244. https://doi.org/10.3389/fevo.2021.651733 (2021).Article 

Google Scholar 
Landstrom, M. T., Heinsohn, R. & Langmore, N. E. Does clutch variability differ between populations of cuckoo hosts in relation to the rate of parasitism?. Anim. Behav. 81, 307–312 (2011).Article 

Google Scholar 
Peterson, S. H. et al. Avian eggshell thickness in relation to egg morphometrics, embryonic development, and mercury contamination. Ecol. Evol. 10, 8715–8740. https://doi.org/10.1002/ece3.6570 (2020).Article 

Google Scholar 
Attard, M., Medina, I., Langmore, N. E. & Sherratt, E. Egg shape mimicry in parasitic cuckoos. J. Evol. Biol. 30, 2079–2084 (2017).CAS 
Article 

Google Scholar 
Birchard, G. F. & Deeming, D. C. Avian eggshell thickness: Scaling and maximum body mass in birds. J. Zool. 279, 95–101. https://doi.org/10.1111/j.1469-7998.2009.00596.x (2009).Article 

Google Scholar 
Orme, D. et al. The caper package: Comparative analysis of phylogenetics and evolution in R. R Packag. Vers. 5, 549–593 (2013).
Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448. https://doi.org/10.1038/nature11631 (2012).ADS 
CAS 
Article 

Google Scholar 
Schliep, K. P. Phangorn: Phylogenetic analysis in R. Bioinformatics 27, 592–593. https://doi.org/10.1093/bioinformatics/btq706 (2011).CAS 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing, (2013). More

Desbruyères, D., Segonzac, M. & Bright, M. Handbook of deep-Sea Hydrothermal Vent Fauna 2nd edn. (Biologiezentrum, 2006).
Google Scholar 
Van Dover, C. L. The Ecology of Deep-Sea Hydrothermal Vents (Princeton University Press, 2000).Book 

Google Scholar 
Tarasov, V. G., Gebruk, A. V., Mironov, A. N. & Moskalev, L. I. Deep-sea and upper sublittoral hydrothermal vent communities: Two different phenomena?. Chem. Geol. 224, 5–39. https://doi.org/10.1016/j.chemgeo.2005.07.021 (2005).ADS 
CAS 
Article 

Google Scholar 
Lonsdale, P. Clustering of suspension-feeding macrobenthos near abyssal hydrothermal vents at oceanic spreading centers. Deep Sea Res. 24, 857–863. https://doi.org/10.1016/0146-6291(77)90478-7 (1977).ADS 
Article 

Google Scholar 
Reid, W. D. et al. Spatial differences in East Scotia Ridge hydrothermal vent food webs: Influences of chemistry, microbiology and predation on trophodynamics. PLoS One 8, e65553. https://doi.org/10.1371/journal.pone.006555 (2013).ADS 
CAS 
Article 

Google Scholar 
Levin, L. A. et al. Hydrothermal vents and methane seeps: Rethinking the sphere of influence. Front. Mar. Sci. 3, 72. https://doi.org/10.3389/fmars.2016.00072 (2016).ADS 
Article 

Google Scholar 
Mullineaux, L. S. et al. Exploring the ecology of deep-sea hydrothermal vents in a metacommunity framework. Front. Mar. Sci. 5, 49. https://doi.org/10.3389/fmars.2018.00049 (2018).Article 

Google Scholar 
Tarasov, V. G. Effects of shallow-water hydrothermal venting on biological communities of coastal marine ecosystems of the western Pacific. Adv. Mar. Biol. 50, 267–421. https://doi.org/10.1016/S0065-2881(05)50004-X (2006).CAS 
Article 

Google Scholar 
Dando, P. R. Biological communities at marine shallow-water vent and seep sites. In The Vent and Seep Biota (ed. Kiel, S.) 333–378 (Springer, 2010).Chapter 

Google Scholar 
Couto, R. P., Rodriguesa, A. S. & Neto, A. I. Shallow-water hydrothermal vents in the Azores (Portugal). J. Integr. Coast. Zone Manage. 15, 495–505. https://doi.org/10.5894/rgci584 (2015).Article 

Google Scholar 
Bellec, L. et al. Microbial communities of the shallow-water hydrothermal vent near Naples, Italy, and chemosynthetic symbionts associated with a free-living marine nematode. Front. Microbiol. 11, 2023. https://doi.org/10.3389/fmicb.2020.02023 (2020).Article 

Google Scholar 
Chan, B. K. K. et al. Community structure of macrobiota and environmental parameters in shallow water hydrothermal vents off Kueishan Island, Taiwan. PLoS One 11, e0148675. https://doi.org/10.1371/journal.pone.0148675 (2016).CAS 
Article 

Google Scholar 
Donnarumma, L. et al. Environmental and benthic community patterns of the shallow hydrothermal area of Secca Delle Fumose (Baia, Naples, Italy). Front. Mar. Sci. 6, 685. https://doi.org/10.3389/fmars.2019.00685 (2019).Article 

Google Scholar 
Southward, A. J. et al. On the biology of submarine caves with sulphur springs: Appraisal of 13C/12C ratios as a guide to trophic relations. J. Mar. Biol. Ass. UK 76, 265–285. https://doi.org/10.1017/S002531540003054X (1996).CAS 
Article 

Google Scholar 
Southward, A. J. et al. Behaviour and feeding of the Nassariid gastropod Cyclope neritea, abundant at hydrothermal brine seeps off Milos (Aegean Sea). J. Mar. Biol. Ass. UK 77, 753–771. https://doi.org/10.1017/S0025315400036171 (1997).Article 

Google Scholar 
Chang, N. N. et al. Trophic structure and energy flow in a shallow-water hydrothermal vent: Insights from a stable isotope approach. PLoS One 13, e0204753. https://doi.org/10.1371/journal.pone.0204753 (2018).CAS 
Article 

Google Scholar 
Trager, G. C. & DeNiro, M. J. Chemoautotrophic sulphur bacteria as a food source for mollusks at intertidal hydrothermal vents: Evidence from stable isotopes. Veliger 33, 359–362 (1990).
Google Scholar 
Kharlamenko, V. I., Zhukova, N. V., Khotimchenko, S. V., Svetashev, V. I. & Kamenev, G. M. Fatty acids as markers of food sources in a shallow-water hydrothermal ecosystem (Kraternaya Bight, Yankich Island, Kurile Islands). Mar. Ecol. Progr. Ser. 120, 231–241. https://doi.org/10.3354/meps120231 (1995).ADS 
CAS 
Article 

Google Scholar 
Chen, C. T. A. et al. Investigation into extremely acidic hydrothermal fluids off Kueishantao Islet, Taiwan. Acta. Oceanol. Sin. 24, 125–133 (2005).CAS 

Google Scholar 
Wang, T. W., Chan, T. Y. & Chan, B. K. K. Trophic relationships of hydrothermal vent and non-vent communities in the upper sublittoral and upper bathyal zones off Kueishan Island, Taiwan: A combined morphological, gut content analysis and stable isotope approach. Mar. Biol. 161, 2447–2463. https://doi.org/10.1007/s00227-014-2479-6 (2014).Article 

Google Scholar 
Chen, C., Chan, T. Y. & Chan, B. K. K. Molluscan diversity in shallow water hydrothermal vents off Kueishan Island, Taiwan. Mar. Biodivers. 48, 709–714. https://doi.org/10.1007/s12526-017-0804-2 (2017).Article 

Google Scholar 
Lebrato, M. et al. Earthquake and typhoon trigger unprecedented transient shifts in shallow hydrothermal vents biogeochemistry. Sci. Rep. 9, 16926. https://doi.org/10.1038/s41598-019-53314-y (2019).ADS 
CAS 
Article 

Google Scholar 
Lin, Y.-S. et al. Intense but variable autotrophic activity in a rapidly flushed shallow-water hydrothermal plume (Kueishantao Islet, Taiwan). Geobiology 19, 87–101. https://doi.org/10.1111/gbi.12418 (2021).CAS 
Article 

Google Scholar 
Jeng, M. S., Ng, N. K. & Ng, P. K. L. Hydrothermal vent crabs feast on sea ‘snow’. Nature 432, 969. https://doi.org/10.1038/432969a (2004).ADS 
CAS 
Article 

Google Scholar 
Ho, T. W., Hwang, J. S., Cheung, M. K., Kwan, H. S. & Wong, C. K. Dietary analysis on the shallow-water hydrothermal vent crab Xenograpsus testudinatus using Illumina sequencing. Mar. Biol. 162, 1787–1798. https://doi.org/10.1007/s00227-015-2711-z (2015).CAS 
Article 

Google Scholar 
Yang, S. H. et al. Bacterial community associated with organs of shallow hydrothermal vent crab Xenograpsus testudinatus near Kueishan Island, Taiwan. PLoS One 11, e0150597. https://doi.org/10.1371/journal.pone.0150597 (2016).CAS 
Article 

Google Scholar 
Wu, J.-Y. et al. Isotopic niche differentiation in benthic consumers from shallow-water hydrothermal vents and nearby non-vent rocky reefs in northeastern Taiwan. Prog. Oceanogr. 195, 102596. https://doi.org/10.1016/j.pocean.2021.102596 (2021).Article 

Google Scholar 
Collin, R. Calyptraeidae from the northeast Pacific (Gastropoda: Caenogastropoda). Zoosymposia 13, 28. https://doi.org/10.11646/zoosymposia.13.1.12 (2019).Article 

Google Scholar 
Phillips, B. T. Beyond the vent: New perspectives on hydrothermal plumes and pelagic biology. Deep-Sea Res. II: Top. Stud. Oceanogr. 137, 480–485. https://doi.org/10.1016/j.dsr2.2016.10.005 (2017).ADS 
CAS 
Article 

Google Scholar 
Portail, M. et al. Food-web complexity across hydrothermal vents on the Azores triple junction. Deep-Sea Res. I: Oceanogr. Res. Pap. 131, 101–120. https://doi.org/10.1016/j.dsr.2017.11.010 (2018).ADS 
CAS 
Article 

Google Scholar 
Nomaki, H. et al. Nutritional sources of meio- and macrofauna at hydrothermal vents and adjacent areas: Natural-abundance radiocarbon and stable isotope analyses. Mar. Ecol. Prog. Ser. 622, 49–65. https://doi.org/10.1016/j.dsr.2017.11.010 (2019).ADS 
CAS 
Article 

Google Scholar 
Alfaro-Lucas, J. M. et al. High environmental stress and productivity increase functional diversity along a deep-sea hydrothermal vent gradient. Ecology 101, e03144. https://doi.org/10.1002/ecy.3144 (2020).CAS 
Article 

Google Scholar 
Stock, B. C. et al. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ 6, e5096. https://doi.org/10.7717/peerj.5096 (2018).Article 

Google Scholar 
Michener, R. H. & Kaufman, L. Stable isotope ratios as tracers in marine food webs: An update. In Stable Isotopes in Ecology and Environmental Science (eds Michener, R. & Lajtha, K.) 238–283 (Blackwell Pub, 2007). https://doi.org/10.1002/9780470691854.ch9.Chapter 

Google Scholar 
Montoya, J. P. Natural abundance of 15N in marine planktonic ecosystems. In Stable Isotopes in Ecology and Environmental Science (eds Michener, R. & Lajtha, K.) 176–201 (Blackwell Pub, 2007). https://doi.org/10.1002/9780470691854.ch7.Chapter 

Google Scholar 
Dietl, G. P. First report of cannibalism in Triplofusus giganteus (Gastropoda: Fasciolariidae). Bull. Mar. Sci. 73, 757–761 (2003).ADS 

Google Scholar 
Cumplido, M., Pappalardo, P., Fernandez, M., Averbuj, A. & Bigatti, G. Embryonic development, feeding and intracapsular oxygen availability in Trophon geversianus (Gastropoda: Muricudae). J. Molluscan. Stud. 77, 429–436. https://doi.org/10.1093/mollus/eyr025 (2011).Article 

Google Scholar 
Modica, M. V. & Holford, M. The neogastropoda: Evolutionary innovations of predatory marine snails with remarkable pharmacological potential. In Evolutionary Biology—Concepts, Molecular and Morphological Evolution (ed. Pontarotti, P.) 249–270 (Springer, 2010).Chapter 

Google Scholar 
Sebens, K. P. Recruitment and habitat selection in the intertidal sea anemones, Anthopleura elegantissima (Brandt) and A. xanthogrammica (Brandt). J. Exp. Mar. Biol. Ecol. 59, 103–124. https://doi.org/10.1016/0022-0981(82)90110-1 (1982).Article 

Google Scholar 
Naumann, M. S., Orejas, C., Wild, C. & Ferrier-Pages, C. First evidence for zooplankton feeding sustaining key physiological processes in a scleractinian cold-water coral. J. Exp. Mar. Biol. Ecol. 214, 3570–3576. https://doi.org/10.1242/jeb.061390 (2011).CAS 
Article 

Google Scholar 
Dodds, L. A., Roberts, J. M., Taylor, A. C. & Marubini, F. Metabolic tolerance of the cold-water coral Lophelia pertusa (Scleractinia) to temperature and dissolved oxygen change. J. Exp. Mar. Biol. Ecol. 349, 205–214. https://doi.org/10.1016/j.jembe.2007.05.013 (2007).CAS 
Article 

Google Scholar 
Quesada, A. J., Acuña, F. H. & Cortés, J. Diet of the sea anemone Anthopleura nigrescens: Composition and variation between daytime and nighttime high tides. Zool. Stud. 53, 26. https://doi.org/10.1186/s40555-014-0026-2 (2014).Article 

Google Scholar 
Ferrier-Pagès, C., Witting, J., Tambutté, E. & Sebens, K. P. Effect of natural zooplankton feeding on the tissue and skeletal growth of the scleractinian coral Stylophora pistillata. Coral Reefs 22, 229–240. https://doi.org/10.1007/s00338-003-0312-7 (2003).Article 

Google Scholar 
Teece, M. A., Estes, B., Gelsleichter, E. & Lirman, D. Heterotrophic and autotrophic assimilation of fatty acids by two scleractinian corals, Montastraea faveolata and Porites astreoides. Limnol. Oceanogr. 56, 1285–1296. https://doi.org/10.4319/lo.2011.56.4.1285 (2011).ADS 
CAS 
Article 

Google Scholar 
Pawlik, J. R. & Deignan, L. K. Cowries graze verongid sponges on Caribbean reefs. Coral Reefs 34, 663. https://doi.org/10.1007/s00338-015-1279-x (2015).ADS 
Article 

Google Scholar 
Chan, B. K. K., Shao, K. T., Shao, Y. T. & Chang, Y. W. A simplified, economical, and robust light trap for capturing benthic and pelagic zooplankton. J. Exp. Mar. Biol. Ecol. 482, 25–32. https://doi.org/10.1016/j.jembe.2016.04.003 (2016).Article 

Google Scholar 
Viozzi, M. F., Martinex del Rio, C. & Williner, V. Tissue-specific isotopic incorporation turnover rates and trophic discrimination factors in the freshwater shrimp Macrobrachium borellii (Crustacea: Decapoda: Palaemonidae). Zool. Stud. 60, 28. https://doi.org/10.6620/ZS.2021.60-28 (2021).CAS 
Article 

Google Scholar 
Tixier, P. et al. Importance of toothfish in the diet of generalist subantarctic killer whales: Implications for fisheries interactions. Mar. Ecol. Prog. Ser. 613, 197–210. https://doi.org/10.3354/meps12894 (2019).ADS 
CAS 
Article 

Google Scholar 
Nolan, E. T., Roberts, C. G. & Britton, R. J. Predicting the contributions of novel marine prey resources from angling and anadromy to the diet of a freshwater apex predator. Freshw. Biol. 64, 1542–1554. https://doi.org/10.1111/fwb.13326 (2019).Article 

Google Scholar 
Stock, B. C. & Semmens, B. X. MixSIAR GUI user manual. Version 3.1. 716. https://doi.org/10.5281/zenodo.561 (2016).R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org (2019).McCutchan, J. H. Jr., Lewis, W. M. Jr., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen and sulfur. Oikos 102, 378–390. https://doi.org/10.1034/j.1600-0706.2003.12098.x (2003).CAS 
Article 

Google Scholar 
Gelman, A. Analysis of variance—why it is more important than ever. Ann. Stat. 33, 1–53. https://doi.org/10.1214/009053604000001048 (2005).MathSciNet 
Article 
MATH 

Google Scholar 
Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis (CRC Press, 2014).MATH 

Google Scholar 
Jackson, A. L., Parnell, A. C., Inger, R. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602. https://doi.org/10.1111/j.1365-2656.2011.01806.x (2011).Article 

Google Scholar  More

Identification of two Zn/Co responsive proteins in diatomsZn and Co growth rate experiments in which Zn or Co (omitting the other) were added to the growth media were conducted and harvested for proteomic analysis. Growth rates of the marine diatom species Thalassiosira pseudonana CCMP1335, Phaeodactylum tricornutum CCMP632, Pseudo-nitzschia delicatissima UNC1205 and Chaetoceros sp. RS19 (Chaetoceros RS19 herein) were conducted in a consistent media composition to allow for intercomparison among species (see “Methods”). The onset of growth limitation by Zn and Co was evident by decreased growth rates under low [Zn2+] and [Co2+], and the ability to use Co to restore Zn-limited growth was species-specific and consistent with prior results for the diatoms T. pseudonana, P. tricornutum and P. delicatissima (Fig. 1a, b)9 and for other eukaryotic algae2,8,10. Growth rates of Chaetoceros RS19 were not stimulated by increasing [Co2+] up to 23.5 pM in the absence of added Zn. This inability to substitute Co for Zn in Chaetoceros RS19 was clearly distinct from that of other diatoms, but was consistent with previous observations in Chaetoceros calcitrans10, implying a genus-wide attribute.Fig. 1: Growth responses of diatoms to varying [Zn2+] and [Co2+] and initial detection of ZCRPs in T. pseudonana.Growth rates of four diatoms over a range of a [Zn2+] and b [Co2+]. Data are presented as mean values of biological duplicate cultures. Data is available in Supplementary Table 1. Global proteomic analyses comparing the proteomes of pooled biological duplicate cultures (n = 2) of T. pseudonana in c high vs. low added Zn and d high vs. low added Co. Each point is an identified protein with the mean of technical triplicate abundance scores in one treatment plotted against the mean of abundance scores in another treatment. The solid line denotes 1:1 abundance. Error bars in c are the standard deviation of technical triplicate measurements.Full size imageThe proteome as a function of Zn2+ and Co2+ was explored in the marine diatom T. pseudonana harvested during log phase growth. Global proteomic analysis comparing low (1.1 pM) versus high (10.2 pM) added [Zn2+] and low (2.3 pM) versus high (23.4 pM) added [Co2+] revealed two uncharacterized diatom proteins that greatly increased in abundance at low [Zn2+] or [Co2+] (Fig. 1c, d). These proteins were annotated as a CobW/HypB/UreG, nucleotide binding domain and a bacterial extra-cellular solute binding domain, respectively, within the manually curated JGI Thaps3 T. pseudonana genome17 and were identified in T. pseudonana cultures with high confidence (≥9 exclusive unique peptides, 100% protein probability; Supplementary Fig. 1). BLAST sequence alignments showed these proteins to be homologous with CobW-like proteins (with 31.69% identity relative to Pseudomonas denitrificans CobW) and with the bacterial nickel transport protein NikA (with 30.5% identity relative to E. coli NikA), respectively. Based on their clear response to Zn and Co in the proteomes of multiple diatom species (Fig. 2a–d), the lack of definitive annotations in diatoms, and their genetic distance from bacterial homologs, these proteins are referred to as ZCRP-A and ZCRP-B (Zn/Co Responsive Protein A and B) in this study. Abundance patterns of these proteins were also investigated in P. tricornutum, P. delicatissima and Chaetoceros RS19. ZCRP-A spectral abundance counts were significantly (Kendall correlation, p  10 times. j Topology predictions from five sub-methods (OCTOPUS, Philius, PolyPhobius, SCAMPI, and SPOCTOPUS), consensus prediction (TOPCONS), and predicted ΔG values for P. tricornutum ZCRP-B generated using the TOPCONS webserver (https://topcons.cbr.su.se/)27,28. k Extent of Co uptake after 24 h for wild-type (WT), ZCRPA-knockout (KO), and ZCRPA-overexpression (OE) lines of P. tricornutum normalized to fluorescence units (fsu). Data are presented as mean values ± the standard deviation of biological triplicate cultures (n = 3). Individual data points are overlaid as white circles. The extent of Co uptake was found to be significantly larger in the ZCRPA-OE line compared to the wild-type via one-way ANOVA (f(3) = 23.16, p = 0.000268) and post hoc Dunnett test (p = 0.00048).Full size imageTo date, connections between COG0523 proteins and utilization of Zn and Co have been explored primarily in prokaryotic organisms. For example, the COG0523 protein CobW has a role in vitamin B12 biosynthesis and thus Co use19,21. In contrast, a subgroup of other COG0523 proteins (YjiA, YeiR, ZigA, and ZagA) have been implicated in Zn2+ metabolism8,13,14,15,16, and a client protein to the metallochaperone ZagA in Bacillus subtilis has been identified22.Compared to bacteria, less is known about the function of COG0523 proteins in marine phytoplankton, though COG0523 protein family members are known to occur in all kingdoms8,23. A recent study described the presence of COG0523 domain proteins upregulated under low Zn in the coccolithophore Emiliania huxleyi, but without further functional characterization24, implying a potential Zn-related function of a COG0523 protein in a marine alga distinct from the marine diatoms included in this study.Although various proteins belonging to the COG0523 subgroup share similar conserved domains, they possess different metal binding abilities and thus likely have different functions among the diverse organisms in which they are found. For example, recent work has established that CobW preferentially binds Co2+ as the cognate metal and acts as a Co2+ chaperone ultimately supplying vitamin B12 in bacteria, whereas the closely related putative metal chaperones YeiR and YjiA (homologs of CobW) bind Zn2+19. We can infer from homology and the response to low Zn and low Co in the present study that Zn2+ and Co2+ are likely both cognate metals for diatom ZCRP-A. Further metal binding and affinity assays can confirm and characterize metal binding in this protein.Frustule morphologyPhenotypic plasticity in P. tricornutum is well documented. Two basic cell morphotypes, fusiform and triradiate, are found in natural liquid environments. It is thought that by adopting the triradiate form, a cell increases its surface area and thus the area of membrane available for enzymatic activity or molecular diffusion of dissolved inorganic carbon (DIC) into the cell. The triradiate form is known to be more common under DIC limiting conditions, which supports this hypothesis25. Distinct morphological differences resulted from the knockout (KO) of the ZCRP-A gene. In P. tricornutum, ZCRP-A knockout cells consistently adopted a triradiate shape while wild-type cells were fusiform (Fig. 4i). Normally, triradiate cells of P. tricornutum spontaneously revert to fusiform across generations26, thus it is notable that ZCRP-A knockout cells have consistently maintained their triradiate shape for 4+ years in culture irrespective of media [Zn2+]. As Zn2+ is the predominant metal cofactor used in diatom CAs, the adoption of the triradiate form in knockout P. tricornutum cells may be a response to a disruption of the carbon concentrating mechanism caused by a reduction in Zn acquisition capability due to ZCRP-A knockout. This is consistent with the observed relative increase in Mn2+-utilizing CA (ι-CA) in the knockout line compared to the wild-type (Supplementary Fig. 5).ZCRP-B sequence analysis and cellular localizationUnlike COG0523 proteins, the relationship of ZCRP-B abundance to environmental Zn and Co concentrations does not appear to have been previously described. Topology predictions of P. tricornutum ZCRP-B using TOPCONS27,28 revealed a single predicted transmembrane domain near the N-terminus, with the majority of the protein predicted to be oriented outside the membrane (Fig. 4j). Overexpression and fluorescent tagging of ZCRP-B confirmed localization to the cell membrane (Fig. 4e–h; Supplementary Fig. 3b). A single predicted transmembrane domain contrasts with the Zrt/Irt-like divalent metal transporters (ZIPs) in eukaryotic algae, which have 7+ transmembrane domains and are key Zn transporters in many organisms29,30. It is therefore most likely that ZCRP-B is not a transporter itself, but one part of a multi-protein membrane complex and potentially interacts with the ZIP system. A sequence database similarity search (BLASTp, NCBI) found the ZCRP-B protein to be homologous with NikA, a protein subunit of the bacterial ATP-binding cassette (ABC) type Ni transport system protein Nik (30.5% identity with E. coli NikA, E = 7e−49, Supplementary Fig. 6). This transporter is well characterized in bacteria and is comprised of five subunits NikA-E. NikB and NikC are two pore-forming integral inner membrane proteins, NikD and NikE are two inner membrane-associated proteins with ATPase activity, and NikA is the periplasmic component that functions as the initial metal receptor31. No proteins with homology to NikB nor NikC were detected in the P. tricornutum proteomes generated in this study. Two uncharacterized P. tricornutum proteins were homologous with NikD (28.8% identity, E = 1e−14) and NikE (34.9% identity, E = 1.33e−8), though neither had abundance trends similar to ZCRP-B, implying that their function and regulation are independent of ZCRP-B.The sequence of a functionally similar bacterial ABC transport complex, CntABCDF (cobalt nickel transporter, also known as Opp1) from Staphylococcus aureus was also compared to NikA and ZCRP-B (Supplementary Fig. 6). CntA shares 25.6% identity with ZCRP-B (E = 3e−28), and similar to NikA, is an extra-cytoplasmic solute-binding protein that transports Ni, Zn and Co. CntA functions as a Ni/Co acquisition system in Zn-limited S. aureus32. Although the Nik and Cnt systems serve Ni and Co transport in bacteria, ZCRP-B responds to Zn and Co in marine diatoms, which have a significant Zn demand. This may imply a recruitment and repurposing of this bacterial Ni transporter component as part of the Zn acquisition systems during the evolution of marine diatoms.ZCRP-B as a putative high-affinity ligandSequence similarity to the extracellular transport components NikA and CntA (Supplementary Fig. 6), localization to the plasma membrane (Fig. 4b; Supplementary Fig. 3b), and increased abundance under low Zn and Co conditions (Fig. 2b) of P. tricornutum ZCRP-B suggests a metal-binding role as part of a high-affinity transport complex. The induction of ZCRP-B expression at low [Zn2+] (Fig. 2a–c) fits the description of a high-affinity Zn uptake system observed in marine algae that is known to be induced at low free [Zn2+]33,34, suggesting that this protein is involved in an adaptive response to extremely scarce Zn availability. Furthermore, ZCRP-B could contribute to the pool of high-affinity organic ligands that complex dissolved Zn, either by dissociation from living cells or upon cell death by viral lysis and grazing, in the upper water column12,35.The identification of a membrane-associated Zn-Co responsive protein-containing putative metal-binding sites allows us to reconsider the mechanisms of cellular metal uptake in diatoms. Prior physiological experiments observed Zn uptake in marine diatoms to approach the limits of diffusion33, and predicted kinetic control with fast cell surface metal binding and uptake relative to dissociation and release back to the seawater environment36. To enable this transport capability, it was postulated that transporters might be so abundant that the membrane becomes crowded37. Here, the observation of a putative Zn-binding, membrane-associated protein with only 1 predicted transmembrane domain instead implies a separation of the Zn concentrating function at the cell surface relative to its transport into the cell. In this scenario when Zn is scarce, biosynthesis of ZCRP-B increases and is tethered to the cell surface to compete Zn away from natural dissolved Zn ligands35 and/or chelate Zn atoms that make it through the diffusive boundary layer to the membrane. In this manner, ZCRP-B would increase the surface Zn concentration in the vicinity of Zn transporters, and multiple ZCRP-B proteins could supply nearby surface ZIP transporters or be endocytosed, avoiding the predicted membrane crowding of transporters problem. Aristilde and colleagues have previously demonstrated that weak natural Zn-binding ligands containing cysteine do indeed enhance cellular Zn uptake within the diatom Thalassiosira weissflogii, with heightened effects in Zn-limited compared to Zn-replete cells38. They proposed the formation of a transient tertiary complex between the Zn-bound ligand and Zn transporters (ZIPs and heavy metal P-type ATPases) at the cell surface, which could be mediated by a surface-tethered Zn binding ligand such as ZCRP-B. Future studies could examine the mechanism of Zn exchange between ZCRP-B and Zn/Co transporters such as the ZIPs in eukaryotic algae, which were also detected at lower Zn and Co abundances in P. tricornutum but with relatively lower spectral counts (Supplementary Fig. 7a, b), consistent with this model. Furthermore, the proposed mechanism of ZCRP-B binding is similar to that of the high-affinity Fe3+ binding protein ISIP2a, previously characterized in marine algae as an iron starvation-induced protein39. ISIP2a has been characterized as a phytotransferrin involved in endocytosis-mediated high-affinity Fe uptake in P. tricornutum that acts to concentrate Fe at the cell surface and is an extracellular protein anchored to the membrane with one transmembrane domain39. As the protein sequences of P. tricornutum ZCRP-B and ISIP2a share no significant similarity, it is possible that the uptake mechanism of ZCRP-B is similar to that of ISIP2a, but specific to high-affinity Zn and Co uptake rather than Fe. This suggests a common strategy of using extracellular membrane-anchored metal acquisition proteins in marine algae faced with metal limitation.Co uptake in wild-type and mutant diatom strainsAs ZCRP-A and ZCRP-B abundance is related to media [Co2+] (Fig. 2a–d), we investigated differences in the extent of Co uptake after 24 h among Zn/Co-limited wild-type, ZCRP-A knockout, ZCRP-A overexpression, and ZCRP-B overexpression lines of P. tricornutum via addition of the radiotracer 57Co (see methods). The extent of Co uptake among genetically modified P. tricornutum lines was observed to be significantly different via one-way ANOVA (f(3) = 23.16, p = 0.000268). A Dunnet post hoc test revealed that uptake was significantly greater (2.6× larger) in the ZCRP-A overexpression line compared to wild-type (p = 0.00048, Fig. 4k). We interpret this result as the overexpression of ZCRP-A creating a larger intracellular binding capacity for Co, thus protecting it from intracellular sensor or regulatory systems and/or efflux pumps. In contrast, no significant difference in Co uptake rates was observed when comparing ZCRP-A knockout, ZCRP-B overexpression, and wild-type lines, suggesting that P. tricornutum ZCRP-A knockout cells are capable of compensating for knockout to maintain Co metabolism, perhaps through the use of low-affinity transporters33. This is consistent with these uptake experiments being conducted using seawater media with a relatively abundant concentration of Zn (background of 7.7 pM Co and 4.0 nM Zn in the absence of EDTA), thus the use of low-affinity transporters was likely sufficient to acquire Zn and Co for growth, and neither ZCRP-A knockout nor ZCRP-B overexpression would be expected to add any metabolic benefit (Fig. 4k). Moreover, if ZCRP-B is only one part of a multi-protein acquisition and transport complex as hypothesized, overexpression of the single protein may not result in enhanced functionality.Abundance patterns of CAs in two diatomsCarbonic anhydrase enzymes constitute a major reservoir of Zn and Co within marine diatoms7. Within the stroma, intracellular chloroplastic CAs are essential in supplying CO2 to RUBISCO as they convert HCO3−, the predominant species of inorganic carbon in the pyrenoid, into CO240,41. Seven subclasses of CAs have been identified in marine diatoms to date and are designated as alpha, beta, gamma, delta, zeta, theta, and iota (α, β, γ, δ, ζ, θ, and ι). While Zn2+ is the cofactor most commonly used in algal CAs, utilization of both cadmium (Cd2+) and cobalt (Co2+) in place of Zn2+ at the active site of ζ-CA (CDCA) and a δ-CA, respectively, has been previously documented2,5,42. Overall, Zn-utilizing CAs increased in abundance with increasing Zn, consistent with the need for rapid HCO3− conversion at faster growth rates (Fig. 5; Supplementary Fig. 7). Specifically, spectral abundance counts of two β-type CAs, PtCA1 and PtCA2, became abundant in high [Co2+] (23.4 pM) and [Zn2+] ( > 1.1 pM) and were inversely related to ZCRP-A abundance (Supplementary Fig. 7). Both PtCA1 and PtCA2 are known to localize to the chloroplast pyrenoid41,43. Moreover, the increasing abundance trends of the Zn-utilizing α-CAs (CA-II and CA-VI) and the θ-CA Pt43233, which localize to the periplastidial compartment, chloroplast endoplasmic reticulum, and thylakoid lumen, respectively, at higher and Zn/Co provide further evidence for this strategy of increasing CA use under Zn-replete and higher growth rate conditions (Fig. 5; Supplementary Fig. 7)43,44.Fig. 5: Comparison of α-CA, ι-CA, and ZCRP abundances.Spectral counting abundance scores of a alpha CA, iota CA, and b ZCRP-A and ZCRP-B detected in Zn and Co treatments of P. tricornutum measured by global proteomic analysis. Data are plotted as means ± the standard deviation of technical triplicate measurements of pooled biological duplicate cultures (n = 2). Protein names are shown with their corresponding JGI protein ID.Full size imageIn contrast, abundance trends of the recently discovered ι-CA were inversely related to Zn2+ (Fig. 5). Originally identified in T. pseudonana, ι-CA was found to localize to the inner chloroplast membrane surrounding the stroma and is unusual in that it prefers Mn2+ to Zn2+ as a cofactor45. In the present study, spectral counts of P. tricornutum ι-CA decreased as metal concentrations increased, similar to that observed for ZCRP-A and ZCRP-B (Fig. 5). This ι-CA response was consistent with a Zn sparing strategy under low [Zn2+] and [Co2+] used to prioritize the use of Zn2+ for other metalloenzyme functions.Due to the inverse relationship between the abundances of ZCRP-A and chloroplastic Zn2+-requiring CAs in P. tricornutum (that is, all CAs detected with the exception of ι-CA) and the various types of CAs in T. pseudonana (Supplementary Fig. 7), it seems unlikely ZCRP-A directly interacts with CAs. These results are instead consistent with the hypothesis that ZCRP-A functions as a Zn2+ allocation and prioritization mechanism during Zn limitation. The role of Zn2+ in key transcriptional and translational proteins such as RNA polymerase and ribosomal proteins is well known, and major reservoirs of Zn are associated with these transcription and translation systems in the fast-growing copiotrophic bacterium Pseudoalteromonas6. The availability of Zn in ribosomes and the ER is therefore likely also a cellular priority in diatoms, and could benefit from utilizing the putative chaperone and trafficking capability of ZCRP-A when Zn is scarce. We, therefore, posit that ZCRP-A may serve as a Zn2+ trafficking or storage protein that contributes to the prioritization and movement of Zn2+ to the ER or CER, while the Mn-utilizing Mn ι-CA compensates for the lowered Zn availability in the chloroplast. The increased biosynthesis of ZCRP-A may be an important function to shift Zn homeostasis, competing for intracellular Zn and trafficking it towards the ER or CER.Distribution of putative ZCRP homologs among oceanic taxaPutative ZCRP homologs among eukaryotic oceanic taxa were identified by BLAST searching the P. tricornutum ZCRP-A and ZCRP-B protein sequences against all available transcriptomes in the Marine Microbial Eukaryotic Transcriptome Sequencing Project (MMETSP) database, which includes over 650 assembled and annotated transcriptomes of oceanic microbial eukaryotes46. Phylogenetic analysis revealed the presence of putative ZCRP-A and ZCRP-B homologs in a wide variety of organisms belonging to the Chromista kingdom that could be further categorized into Bacillariophyceae, Dinophyceae, and Prymnesiophyceae classes (Supplementary Figs. 8 and  9). Notably, the Chaetoceros RS-19 ZCRP-A homolog did not phylogenetically cluster with the other diatoms (Bacillariophyceae), but instead appears to be more closely related to E. coli YjiA (Supplementary Fig. 8). Furthermore, the lack of the conserved G2/Switch I region in the Chaetoceros RS-19 homolog (Fig. 3) is anomalous in comparison to other putative homologs identified within the MMETSP database. Overall, ZCRPs are not exclusive to oceanic diatoms, but rather are widely distributed amongst oceanic taxa.Metaproteomic detection of ZCRP-A and ZCRP-BTo investigate the use of ZCRP-A and ZCRP-B in the natural environment, we searched metaproteomic data collected during the KM1128 METZYME (Metals and Enzymes in the Pacific) research expedition on the R/V Kilo Moana October 1–25, 2011 from Oʻahu, Hawaiʻi, to Apia, Samoa (Fig. 6a). dZn followed a nutrient-like distribution as described previously, with an average surface (40 m) dZn concentration of 1.21 nM and average deep water (3000 m) concentration of 10.37 nM47 (Fig. 6b). dCo was highly depleted in the upper photic zone as the result of biological uptake48,49 (Fig. 6c). Eukaryotic homologs of ZCRP-A and ZCRP-B were detected at multiple stations at surface ( More

To estimate the potential increase in biodiversity decline and the national level of conservation investment needed to counteract it in post-conflict Colombia, we used a model developed by Waldron et al.19. This quantitative model predicts national biodiversity status change, the biodiversity decline score (BDS), based on investment in conservation actions in relation to human development pressures. The model uses seven predictors related to the economy of each country, its biodiversity status or dynamics, and its conservation spending19.ScenariosWe used the Waldron et al.19 model to predict (1) the expected increase in biodiversity decline immediately after the peace agreement (the post-conflict period), (2) the conservation funding needed to prevent this additional decline and (3) the investment necessary to avoid biodiversity decline. We used four scenarios to examine our questions.The baseline scenario was the War BDS scenario, which estimated the BDS of the last 12 years of the conflict, before the peace agreement in 2016. Predictor variables related to human pressures were from 4–5 years before to appropriately represent the lag in the modelled effect19. We used the most recent available value of ‘strict-sense’ conservation investment19. The following three scenarios examined post-conflict options and were compared with this War BDS scenario.The Peace BDS scenario predicted the BDS for a 12-year period post-conflict. The predictor variables related to human pressures were from the 11-year period immediately after the peace agreement. We assumed the same conservation spending as for the War BDS. The Lower BDS scenario estimated the necessary investment to achieve the War BDS. This represented a situation where the biodiversity loss during the conflict did not change post-conflict. For this scenario, we held the human pressure variables the same as in the Peace BDS scenario. The Prevented BDS scenario was exactly the same as the Lower BDS scenario, but we set a target of no biodiversity decline (BDS = 0).We used the War and Peace BDS estimates to calculate the expected additional biodiversity decline post-conflict. Then, we used the model with data from the Lower BDS scenario to calculate the investment needed to prevent any additional biodiversity decline post-conflict. Finally, we used data from the Prevented BDS scenario to estimate the conservation investment necessary to halt biodiversity decline in the post-conflict period.Data for predictor variablesWe modified the predictors related to agriculture and economic growth to examine anticipated changes in human pressures. This revision allowed us to consider the expected agricultural expansion, in the form of percentage of agricultural land and growth, and economic growth, as the gross domestic product (GDP) and GDP growth. We also modified the function so that we could use it to estimate funding needs given a target BDS.For the War BDS scenario, data on GDP, GDP growth, agricultural land area and agricultural land area growth were either available or easily computed. The data for GDP and the percentage of agricultural land from 2001–2012 were obtained from The World Bank28. The agricultural land growth was calculated as the difference between the percentage of agricultural land of consecutive years, and GDP growth was calculated from the GDP per capita data from The World Bank28.For the Peace, Lower and Prevented BDS scenarios, we made projections about the predictors. For the GDP we used projections for 2017–2019, and for the GDP growth projections for 2019–2022 (ref. 33), and then selected an annual increase in the GDP growth of 0.3 percentage points for the remaining 5 years, corresponding to the most conservative estimate found in ref. 34. We then used our estimates of GDP growth for the whole time period to calculate the GDP per capita for the last 10 years, and used population projection to compute the GDP for the next 10 years.To estimate the agricultural land and growth for the Peace, Lower and Prevented BDS scenarios, we used projections on deforestation. We developed our model to reflect the immediate consequences in agricultural expansion and deforestation post-conflict. Thus, we estimated the percentage agricultural land area using projected values of deforestation35. We support this approach based on two observations. First, at least 90% of deforested land was transformed to agriculture during past years36. Second, forest transformation to agriculture has been more aggressive since the peace agreement7,10,11. Thus, the processes that fuel agricultural conversion are stronger. For each year we added the deforested area to the previous agricultural land area. We then calculated the yearly percentage agricultural land area and computed the agricultural growth as the percentage difference between the agricultural land area of consecutive years. We took the minimum and maximum values of deforestation projections to create best- and worst-case scenarios.We acknowledge that our use of the Waldron et al.19 model has limitations because we did not update all the predictors. Specifically, two ‘inertia’ terms that account for the effect of biodiversity decline occurring immediately before the time period of interest19. The coefficients associated with these terms have a positive effect on the BDS, which means that a more intense decline in the past will increase the predicted biodiversity decline. Given the increase in human pressures, the actual inertia terms are probably larger than the ones we used. Thus, the Peace BDS and the actual increase in biodiversity decline post-FARC may be larger.The ModelTo create a broad proxy for the expected cost of potential conservation interventions across Colombia, we estimated the OCC for agriculture at the 1 km2 scale. We estimated the OCC by building a spatially explicit probability model of forest conversion to agriculture and then paired it with the net present value of the expected return of different agricultural activities.We calculated the OCC following the methodology proposed by Naidoo and Adamowicz24. Their approach models the expected net present value of potential net rents resulting from agricultural uses of a forested parcel, while accounting for the probability of conversion to agriculture. Provided that each agricultural use k has its own annual expected return per area of land Rk, and that each parcel i has a probability of conversion Pik from forest to agricultural use k, the expected value for a given discount rate δ is$${{{mathrm{OCC}}}} = mathop {sum}limits_{i = 1}^{{I}} {mathop {sum}limits_{k = 1}^{{K}} {{{P}}_{i,k}} } frac{{{{R}}_k}}{{delta }}$$
(1)
Thus, the OCC of an area composed of several parcels is equal to the sum of the expected returns of the probable agricultural uses, weighted according to their probability of conversion, in each of the parcels, summed across all of the parcels.We calculated the OCC for forested areas in three steps. First, we built a probability model to obtain the general risk of forest conversion (Pdef). Next, we built a second model that, given that a parcel had been transformed, predicted the probability of forest conversion to different types of agricultural activities (({{P}}_{{{{mathrm{ag}}}}_k})). We used both models to compute the total probability of conversion to each type of agricultural activity k in a parcel i (({{P}}_{ik} = {{P}}_{{{{mathrm{def}}}}_i} times {{P}}_{{{{mathrm{ag}}}}_{i,k}})). We then estimated the net present value of the expected return of each agricultural activity (Rk/δ) using literature and commercial prices and the costs of agricultural products.Types of agricultural land use modelledOur OCC model needed to represent relevant agricultural activities. Below, we justify our selection of three types of agricultural land uses: cattle ranching, coca crops and other crops.Cattle ranching is expected to be a major driver of post-conflict deforestation11. This activity has accounted for 50% of deforestation, in the form of forest conversion to pasture, in past years36, and has considerably expanded post-conflict7.Illegal coca crops are expected to be, and have been observed to be, an important driver of post-conflict deforestation12. This activity is at risk of increase where the withdrawal of FARC and the absence of state presence left a ‘power vacuum’ that facilitated other illegal groups gaining control of such crops in the territory7,11,12. Indeed, evidence shows that deforestation associated with coca cultivation increased as the conflict became less intense37.Other crops were grouped into a single category with cattle ranching due to their small percentage contribution to forest conversion in our time frame (3%) compared with cattle ranching and coca crops (47 and 50%, respectively). We proxy for the extent of all other crops by using data on the distribution of three relevant agricultural products in the post-conflict period: cacao, oil palm and coffee. The cacao crop has high potential in most of the key post-conflict areas in Colombia, so it could have a major role in the peace transition38. Oil palm is important owing to its steep increase in cultivation during the last few years12, to the point that Colombia is now the largest producer in South America39. The relevance of coffee resides in its impact on the rural population, given that coffee crops are the only source of income for approximately 563,000 families and generates over 726,000 rural jobs40.Landscape features dataWe selected ten factors relevant to deforestation in Colombia to model the probability of forest conversion: proximity to roads, presence of FARC (binary: presence or no presence), population density, slope23, elevation, proximity to deforested areas, to rivers, to mining areas and to oil wells, and belonging to national and regional PAs10. National PAs restrict economic activities and are managed by the System of National Natural Parks, while regional PAs allow multiple-use activities and are managed by regional environmental authorities8,41. We did not include indigenous reserves or Afro-Colombian lands.We used deforested areas from 1990 to 2000 from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM)42, the water bodies map from the Department of Environment and Sustainable Development43 and maps from the Instituto Geográfico Agustín Codazzi (IGAC)44 to calculate the distance to already deforested areas, rivers, roads, mining areas and oil wells. The elevation map was obtained from NASA’s (National Aeronautics and Space Administration’s) Land Topography digital images45, and we calculated the slope using the elevation map. We computed population density as the mean value of the 32 mainland administrative departments from 2000 to 2012 using data from the Departamento Administrativo Nacional de Estadística46 (DANE; see Supplementary Table 3 for dataset details). We obtained a map showing the presence of FARC from the Fundación Paz y Reconciliación (PARES)47. All spatial data calculations were performed using software QGIS (https://www.qgis.org/en/site/, version 3.12.2) and R (https://www.r-project.org/, version 3.6.2).Forest conversion and agricultural use modelWe used a two-stage modelling process. First, we modelled the probability of an area being deforested by any driver (not exclusively due to agricultural expansion), using the total deforested area in the country in a 12-year period to parametrize our model (forest conversion model). Second, we modelled the probability that the deforestation was due to a particular agricultural activity (agricultural use model). To parametrize this second model, we used patches of land that were indeed transformed to an agricultural use in this same 12-year period. We combined these two models to obtain the probability that a patch of land was deforested to a particular agricultural activity.We used a binomial logistic regression model to build our forest conversion model, which estimates the probability of forest conversion (Pdef). We used the land cover change from 2000 to 2012 across the country, available from IDEAM42, and reclassified each pixel cell as forested or transformed. We used the bayesglm function from the R arm package48.For our agricultural use model, we built a second binomial logistic regression model to estimate ({{P}}_{{{{mathrm{ag}}}}_k}), the probability of conversion to each type of agricultural activity (cattle and other crops or coca crops) for a parcel that had been transformed. We employed data on forested areas in 2000 that had been converted by 2012. The coca crops cover map was obtained from the Sistema Integrado de Control de Cultivos Ilícitos (BIESIMCI)49. For the cattle ranching map, we used forested areas converted to pasture. Our other crop data contained temporary and permanent crops obtained from a land cover map43.It should be noted that in logistic regression models, the probability of conversion does not change in a linear fashion, but the ratio of probabilities (odds) does. For the agricultural model, the odds describe the probability of conversion to coca crops over the joint probability of conversion to cattle and other crops. This implies that the variation between the probabilities, not the probability itself, changes constantly.To check for spatial autocorrelation, we plotted spatial correlograms of the models’ residuals with Moran’s I. Because spatial patterns were present, we subsampled for pixel cells at a minimum distance of 20 km between points, which reduced the spatial effects adequately for our purposes, although it was most effective for the forest conversion model (Extended Data Fig. 1). We checked for collinearity in the predictor variables using variance inflation factor scores and removed the variables with a value >3 (distance to mines and oil wells; Supplementary Tables 4 and 5). We performed tenfold cross-validation to test the prediction accuracy of the models. This process splits the data into ten subsets and repeatedly fits the model with the data of nine of the subsets to compare its predictions with the remaining subset. We calculated the percentage of correct predictions (overall accuracy) each time and computed the mean as the final forecasting accuracy indicator.Estimation of annual net rentWe estimated the net present values of the expected return of each agricultural activity to estimate the OCC of forested areas in Colombia. For cattle, we used annual net rent from a beef company50. The total annual net rent for other crops was calculated as the weighted average of the net rents for oil palm, cacao and coffee proportional to their land area in 2016 and 2017 (refs. 51,52,53). For coca crops, we used the average net profit for farmers who sell coca leaves54. We selected three discount rate values: 5, 10 and 20% (Supplementary Tables 6 and 7).Predicting forest conversion and OCCTo predict the probability of forest conversion, we updated our spatial information on roads, deforested areas from 2007 to 2017 (ref. 42), FARC presence as the presence of FARC dissidents and deserters in 2017 (ref. 47), and population density as the mean population density by department from 2017 to 2023 (ref. 55). Together with the annual net rent for each agricultural activity, we used the probabilities of conversion of the two models to compute the OCC, or expected land value, of each forested pixel cell for the three discount rates using Eq. (1).We recognize that the simplified national context of social violence when predicting the probability of forest conversion can limit the application of our results. Our models included FARC presence, and we used the presence of dissidents and deserters in this forecasting stage. However, this ignores other criminal groups that might influence the risk of forest conversion, particularly to coca crops, due to the ‘power vacuum’ left by the withdrawal of FARC and lack of state presence11. Because we overlooked the potential impact of other criminal groups, the probability of forest conversion, particularly to coca crops, could have been underestimated. This would imply an underestimation of the OCC in the areas with presence of these other criminal groups.We used the rural cadastral values56 to validate our OCC results by comparing our predicted mean land values by administrative department in the country. Although rural cadastral values might not reflect the value of illegal coca crops, they were, to the best of our knowledge, the best available data for our purposes.The STAR metricThe STAR metric is a measurement of the potential benefit to threatened and near-threatened species of actions aimed at reducing threats and restoring habitat20. The metric can be disaggregated spatially using the area of habitat for each species, showing the proportional potential contributions of conservation actions in particular regions. We focused on the STAR threat-abatement score (START) only. The START score can be further disaggregated by threat according to the contribution of each threat to the species’ risk of extinction, which allows analysis of potential abatement of species extinction risk by particular activities at particular locations. We took advantage of this trait and used the START metric in a specialized way, focusing on the threats posed by agriculture only on all the species with an area of habitat in Colombia. This resulted in 475 species considered (246 amphibians, 172 birds and 57 mammals), of which 169 are vulnerable, 124 near-threatened, 130 endangered and 52 critically endangered. Agriculture accounted for 52% of the total START. This focus on agriculture includes annual and perennial non-timber crops, wood and pulp plantations, and livestock farming and ranching, so we treated land converted to cattle and crops in the same way even though each land-use type has different impacts on species.The use of the STAR metric has some limitations associated with the spatial distribution of the threat due to agriculture. First, the STAR metric is based on documented ongoing and expected future threats to the species according to the International Union for Conservation of Nature Red List. The majority of documented threats are ongoing, thus the majority of species threatened by agriculture are already being negatively impacted. This causes uncertainty in the assumption that avoiding further agricultural conversion will reduce species extinction risk, as additional activities to mitigate the impact of current agricultural activities on the species may also be required. Nevertheless, species assessed as threatened by agriculture are known to be vulnerable to this pressure, meaning that they would almost certainly suffer negative impacts under future agricultural expansion.Second, there is uncertainty in the potential spatial distribution of agricultural expansion. Therefore, the STAR metric as we used it helped us identify sites with urgent potential benefits of avoiding agriculture. This could under-represent territories of great biodiversity value that are not currently impacted by agriculture, like the Amazon region.Prioritization mapsWe wanted to achieve a coarse methodology that could help decision-makers direct national conservation funding to the territories with the most potential benefits of halting forest conversion to agriculture. To pair the STAR scores with our modelled OCC, we divided the total range of STAR scores and OCC into high, medium and low values. Given the distribution of STAR scores, we divided the total range in the logarithmic scale. We classified each forested pixel cell into one of nine combinations of STAR scores and OCC. This analysis was later translated to the municipality resolution by calculating the mean STAR score and mean OCC of all forested pixel cells in each municipality, and applying the same classification system used at the pixel resolution. The distributions of aggregated STAR scores and OCC at the municipality resolution follow a similar pattern to the distribution by pixel cell, with small differences due to the grouping of the values in means (Extended Data Fig. 2b,c).Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Canadell, J. G. et al. Multi-decadal increase of forest burned area in Australia is linked to climate change. Nat. Commun. 12, 6921 (2021).Nolan, R. H. et al. What do the Australian Black Summer fires signify for the global fire crisis? Fire 4, 97 (2021).Article 

Google Scholar 
Levin, N., Yebra, M. & Phinn, S. Unveiling the factors responsible for Australia’s Black Summer fires of 2019/2020. Fire 4, 58 (2021).Article 

Google Scholar 
Abram, N. J. et al. Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun. Earth Environ. 2, 8 (2021).Keenan, R. et al. No evidence that timber harvesting increased the scale or severity of the 2019/20 bushfires in south-eastern Australia. Aust. For. 84, 133–138 (2021).Article 

Google Scholar 
Fire Severity in Harvested Areas (New South Wales Department of Primary Industry, 2020); https://www.dpi.nsw.gov.au/__data/assets/pdf_file/0020/1222391/fire-severity-in-harvested-areas.pdfLindenmayer, D. B., Kooyman, R. M., Taylor, C., Ward, M. & Watson, J. E. Recent Australian wildfires made worse by logging and associated forest management. Nat. Ecol. Evol. 4, 898–900 (2020).Bowman, D. M., Williamson, G. J., Gibson, R. K., Bradstock, R. A. & Keenan, R. J. The severity and extent of the Australia 2019–20 Eucalyptus forest fires are not the legacy of forest management. Nat. Ecol. Evol. 5, 1003–1010 (2021).Poulos, H. M., Barton, A. M., Slingsby, J. A. & Bowman, D. M. Do mixed fire regimes shape plant flammability and post-fire recovery strategies? Fire 1, 39 (2018).Article 

Google Scholar 
Lindenmayer, D. B. et al. Logging elevated the probability of high-severity fire in the 2019–20 Australian forest fires. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01716-z (2022).Peterson, D. A. et al. Australia’s Black Summer pyrocumulonimbus super outbreak reveals potential for increasingly extreme stratospheric smoke events. NPJ Clim. Atmos. Sci. 4, 38 (2021).Gibson, R., Danaher, T., Hehir, W. & Collins, L. A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest. Remote Sens. Environ. 240, 111702 (2020).Article 

Google Scholar 
Taylor, C., McCarthy, M. A. & Lindenmayer, D. B. Nonlinear effects of stand age on fire severity. Conserv. Lett. 7, 355–370 (2014).Article 

Google Scholar 
Price, O. F. & Bradstock, R. A. The efficacy of fuel treatment in mitigating property loss during wildfires: insights from analysis of the severity of the catastrophic fires in 2009 in Victoria, Australia. J. Environ. Manag. 113, 146–157 (2012).Article 

Google Scholar 
Lindenmayer, D., Taylor, C. & Blanchard, W. Empirical analyses of the factors influencing fire severity in southeastern Australia. Ecosphere 12, e03721 (2021).Article 

Google Scholar 
Bowman, D. M., Williamson, G. J., Prior, L. D. & Murphy, B. P. The relative importance of intrinsic and extrinsic factors in the decline of obligate seeder forests. Global Ecol. Biogeogr. 25, 1166–1172 (2016).Article 

Google Scholar 
Taylor, C., Blanchard, W. & Lindenmayer, D. B. Does forest thinning reduce fire severity in Australian eucalypt forests? Conserv. Lett. 14, e12766 (2021).Article 

Google Scholar 
Lindenmayer, D. B. & Taylor, C. New spatial analyses of Australian wildfires highlight the need for new fire, resource, and conservation policies. Proc. Natl Acad. Sci. USA 117, 12481–12485 (2020).CAS 
Article 

Google Scholar 
Cruz, M., Alexander, M. & Plucinski, M. The effect of silvicultural treatments on fire behaviour potential in radiata pine plantations of South Australia. For. Ecol. Manag. 397, 27–38 (2017).Article 

Google Scholar 
Lindenmayer, D. B., Hobbs, R. J., Likens, G. E., Krebs, C. J. & Banks, S. C. Newly discovered landscape traps produce regime shifts in wet forests. Proc. Natl Acad. Sci. USA 108, 15887–15891 (2011).CAS 
Article 

Google Scholar  More

Oppel, S. et al. Spatial scales of marine conservation management for breeding seabirds. Mar. Policy 98, 37–46 (2018).Article 

Google Scholar 
Lewison, R. et al. Research priorities for seabirds: improving conservation and management in the 21st century. Endanger. Species Res 17, 93–121 (2012).Article 

Google Scholar 
Hasegawa, H. & DeGange, A. R. The Short-tailed Albatross, Diomedea albatrus, its status, distribution and natural history. Am. Birds 36, 806–814 (1982).
Google Scholar 
Tickell, W. L. N. Albatrosses (Pica Press, 2000).BirdLife International. Phoebastria albatrus. The IUCN Red List of Threatened Species, e.T22698335A132642113 https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22698335A132642113.en (2018).Japan Ministry of the Environment. Ministry of the Environment Red List (Government of Japan, 2020).COSEWIC. COSEWIC Assessment and Status Report on the Short-tailed Albatross Phoebastria albatrus in Canada (Committee on the Status of Endangered Wildlife in Canada, 2013).Environment Canada. Recovery Strategy for the Short-tailed Albatross (Phoebastria albatrus) and the Pink-footed Shearwater (Puffinus creatopus) in Canada (Environment Canada, 2008).United States of America Fish and Wildlife Service. Endangered and Threatened Wildlife and Plants; Final Rule to List the Short-tailed Albatross as Endangered in the United States. 65 FR 46643, 46643–4654, Document Number 00–19123 (2000).United States of America Fish and Wildlife Service. Short-tailed Albatross (Phoebastria albatrus) 5-Year Review: Summary and Evaluation (United States of America Fish and Wildlife Service, 2020).United States of America Fish and Wildlife Service. Short-tailed Albaross Recovery Plan (United States of America Fish and Wildlife Service, 2008).Orben, R. A. et al. Ontogenetic changes in at-sea distributions of immature short-tailed albatrosses Phoebastria albatrus. Endanger. Species Res 35, 23–37 (2018).Article 

Google Scholar 
Orben, R. A. et al. Across borders: external factors and prior behaviour influence North Pacific albatross associations with fishing vessels. J. Appl. Ecol. 58, 1272–1283 (2021).Article 

Google Scholar 
Fox, C. H., Robertson, C., O’Hara, P. D., Tadey, R. & Morgan, K. H. Spatial assessment of albatrosses, commercial fisheries, and bycatch incidents on Canada’s Pacific coast. Mar. Ecol. Prog. Ser. 672, 205–222 (2021).Article 

Google Scholar 
Piatt, J. F. et al. Predictable hotspots and foraging habitat of the endangered short-tailed albatross (Phoebastria albatrus) in the North Pacific: implications for conservation. Deep Sea Res. Part II 53, 387–398 (2006).Article 

Google Scholar 
Suryan, R. M. et al. Migratory routes of short-tailed albatrosses: use of exclusive economic zones of North Pacific Rim countries and spatial overlap with commercial fisheries in Alaska. Biol. Conserv. 137, 450–460 (2007).Article 

Google Scholar 
Suryan, R. M. & Fischer, K. N. Stable isotope analysis and satellite tracking reveal interspecific resource partitioning of nonbreeding albatrosses off Alaska. Can. J. Zool. 88, 299–305 (2010).CAS 
Article 

Google Scholar 
Zador, S. G., Punt, A. E. & Parrish, J. K. Population impacts of endangered short-tailed albatross bycatch in the Alaskan trawl fishery. Biol. Conserv. 141, 872–882 (2008).Article 

Google Scholar 
Geernaert, T. O., Gilroy, H. L., Kaimmer, S. M., Williams, G. H. & Trumble, R. J. A Feasibility Study that Investigates Options for Monitoring Bycatch of the Short-tailed Albatross in the Pacific Halibut Fishery off Alaska (International Pacific Halibut Commission, 2001).Guy, T. J. et al. Overlap of North Pacific albatrosses with the U.S. west coast groundfish and shrimp fisheries. Fish. Res. 147, 222–234 (2013).Article 

Google Scholar 
Bolnick, D. I. et al. The ecology of individuals: incidence and implications of individual specialization. Am. Natural 161, 1–28 (2003).Article 

Google Scholar 
Votier, S. C. et al. Individual responses of seabirds to commercial fisheries revealed using GPS tracking, stable isotopes and vessel monitoring systems. J. Appl. Ecol. 47, 487–497 (2010).Article 

Google Scholar 
Wakefield, E. D. et al. Long-term individual foraging site fidelity—why some gannets don’t change their spots. Ecology 96, 3058–3074 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
Votier, S. C. et al. Effects of age and reproductive status on individual foraging site fidelity in a long-lived marine predator. Proc. R. Soc. B 284, 20171068 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Sztukowski, L. A. et al. Sex differences in individual foraging site fidelity of Campbell albatross. Mar. Ecol. Prog. Ser. 601, 227–238 (2018).Article 

Google Scholar 
Gutowsky, S. E. et al. Divergent post-breeding distribution and habitat associations of fledgling and adult Black-footed Albatrosses Phoebastria nigripes in the North Pacific. Ibis 156, 60–72 (2014).Article 

Google Scholar 
Weimerskirch, H., Åkesson, S. & Pinaud, D. Postnatal dispersal of wandering albatrosses Diomedea exulans: implications for the conservation of the species. J. Avian Biol. 37, 23–28 (2006).
Google Scholar 
Olson, S. L. & Hearty, P. J. Probable extirpation of a breeding colony of Short-tailed Albatross (Phoebastria albatrus) on Bermuda by Pleistocene sea-level rise. Proc. Natl Acad. Sci. 100, 12825–12829 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dall, W. H. Notes on pre-historic remains in the Aleutian islands. Proc. Calif. Acad. Sci. 4, 283–287 (1872).
Google Scholar 
Eda, M. et al. Inferring the ancient population structure of the vulnerable albatross Phoebastria albatrus, combining ancient DNA, stable isotope, and morphometric analyses of archaeological samples. Conserv. Genet. 13, 143–151 (2012).Article 

Google Scholar 
Cousins, K. L., Dalzell, P. & Gilman, E. Managing pelagic longline-albatross interactions in the North Pacific Ocean. Mar. Ornithol 28, 159–174 (2000).
Google Scholar 
Hobson, K. A. & Montevecchi, W. A. Stable isotopic determinations of trophic relationships of great auks. Oecologia 87, 528–531 (1991).PubMed 
Article 
PubMed Central 

Google Scholar 
Fuller, B. T. et al. Pleistocene paleoecology and feeding behavior of terrestrial vertebrates recorded in a pre-LGM asphaltic deposit at Rancho La Brea, California. Palaeogeogr. Palaeoclimatol. Palaeoecol. 537, 109383 (2020).Article 

Google Scholar 
Hobson, K. A. & Clark, R. G. Assessing avian diets using stable isotopes I: turnover of 13C in tissues. Condor 94, 181–188 (1992).Article 

Google Scholar 
Hyland, C., Scott, M. B., Routledge, J. & Szpak, P. Stable carbon and nitrogen isotope variability of bone collagen to determine the number of isotopically distinct specimens. J. Archaeol. Method Theory https://doi.org/10.1007/s10816-021-09533-7 (2021).Article 

Google Scholar 
Hedges, R. E. M., Clement, J. G., Thomas, D. L. & O’Connell, T. C. Collagen turnover in the adult femoral mid‐shaft: modeled from anthropogenic radiocarbon tracer measurements. Am. J. Phys. Anthropol. 133, 808–816 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
Guiry, E. J., Orchard, T. J., Royle, T. C. A., Cheung, C. & Yang, D. Y. Dietary plasticity and the extinction of the passenger pigeon (Ectopistes migratorius). Quat. Sci. Rev. 233, 106225 (2020).Article 

Google Scholar 
Minagawa, M. & Wada, E. Stepwise enrichment of 15N along food chains: further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140 (1984).CAS 
Article 

Google Scholar 
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).CAS 
Article 

Google Scholar 
Hobson, K. A., Ambrose, W. G. Jr & Renaud, P. E. Sources of primary production, benthic-pelagic coupling, and trophic relationships within the Northeast Water Polynya: insights from δ13C and δ15N analysis. Mar. Ecol. Prog. Ser. 128, 1–10 (1995).Article 

Google Scholar 
Sigman, D., Karsh, K. & Casciotti, K. Ocean process tracers: nitrogen isotopes in the ocean in Encyclopedia of Ocean Science (eds Steele, J. H. et al.) 4139–4152 (Academic Press, 2009).Guiry, E. Complexities of stable carbon and nitrogen isotope biogeochemistry in ancient freshwater ecosystems: implications for the study of past subsistence and environmental change. Front. Ecol. Evol 7, 313 (2019).Article 

Google Scholar 
Rau, G. H., Takahashi, T. & Des Marais, D. J. Latitudinal variations in plankton δ13C: implications for CO2 and productivity in past oceans. Nature 341, 516–518 (1989).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Popp, B. N. et al. Effect of phytoplankton cell geometry on carbon isotopic fractionation. Geochim. Cosmochim. Acta 62, 69–77 (1998).CAS 
Article 

Google Scholar 
Laws, E. A., Popp, B. N., Bidigare, R. R., Kennicutt, M. C. & Macko, S. A. Dependence of phytoplankton carbon isotopic composition on growth rate and (CO2) aq: theoretical considerations and experimental results. Geochim. Cosmochim. Acta 59, 1131–1138 (1995).CAS 
Article 

Google Scholar 
Vokhshoori, N. L. et al. Broader foraging range of ancient short-tailed albatross populations into California coastal waters based on bulk tissue and amino acid isotope analysis. Mar. Ecol. Prog. Ser. 610, 1–13 (2019).CAS 
Article 

Google Scholar 
Sherwood, O. A., Lehmann, M. F., Schubert, C. J., Scott, D. B. & McCarthy, M. D. Nutrient regime shift in the western North Atlantic indicated by compound-specific δ15N of deep-sea gorgonian corals. Proc. Natl Acad. Sci. 108, 1011–1015 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Szpak, P., Savelle, J. M., Conolly, J. & Richards, M. P. Variation in late holocene marine environments in the Canadian Arctic Archipelago: evidence from ringed seal bone collagen stable isotope compositions. Quat. Sci. Rev. 211, 136–155 (2019).Article 

Google Scholar 
Guiry, E. J. et al. Deforestation caused abrupt shift in Great Lakes nitrogen cycle. Limnol. Oceanogr. 65, 1921–1935 (2020).CAS 
Article 

Google Scholar 
Wiley, A. E. et al. Millennial-scale isotope records from a wide-ranging predator show evidence of recent human impact to oceanic food webs. Proc. Natl Acad. Sci. 110, 8972–8977 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Keeling, C. D. The Suess effect: 13Carbon-14Carbon interrelations. Environ. Int. 2, 229–300 (1979).CAS 
Article 

Google Scholar 
McMahon, K. W., Thorrold, S. R., Elsdon, T. S. & McCarthy, M. D. Trophic discrimination of nitrogen stable isotopes in amino acids varies with diet quality in a marine fish. Limnol. Oceanogr. 60, 1076–1087 (2015).CAS 
Article 

Google Scholar 
Chikaraishi, Y. et al. Determination of aquatic food‐web structure based on compound‐specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750 (2009).CAS 
Article 

Google Scholar 
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER–stable isotope Bayesian ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
Ambrose, S. H. Preparation and characterization of bone and tooth collagen for isotopic analysis. J. Archaeol. Sci. 17, 431–451 (1990).Article 

Google Scholar 
Guiry, E. J. & Szpak, P. Improved quality control criteria for stable carbon and nitrogen isotope measurements of ancient bone collagen. J. Archaeol. Sci. 132, 105416 (2021).CAS 
Article 

Google Scholar 
Thompson, D. R. & Furness, R. W. Stable-isotope ratios of carbon and nitrogen in feathers indicate seasonal dietary shifts in Northern Fulmars. Auk 112, 493–498 (1995).Article 

Google Scholar 
Carter, H. R. & Sealy, S. G. Historical occurrence of the short-tailed Albatross in British Columbia and Washington. 1841–1958. Wildl. Afield 11, 24–38 (2014).
Google Scholar 
Crockford, S. The Archaeological History of Short-tailed Albatross in British Columbia: A Review and Summary of STAL Skeletal Remains, as Compared to Other Avian Species, Identified from Historic and Prehistoric Midden Deposits. Report on file, Canadian Wildlife Service (2003).Borrmann, R. M., Phillips, R. A., Clay, T. A. & Garthe, S. High foraging site fidelity and spatial segregation among individual great black-backed gulls. J. Avian Biol. 50, e02156 (2019).Article 

Google Scholar 
Wilkinson, B. P., Haynes-Sutton, A. M., Meggs, L. & Jodice, P. G. High spatial fidelity among foraging trips of Masked Boobies from Pedro Cays, Jamaica. PLoS ONE 15, e0231654 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Araújo, M. S., Bolnick, D. I. & Layman, C. A. The ecological causes of individual specialisation. Ecol. Lett. 14, 948–958 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
Grémillet, D. et al. Offshore diplomacy, or how seabirds mitigate intra-specific competition: a case study based on GPS tracking of Cape gannets from neighbouring colonies. Mar. Ecol. Prog. Ser. 268, 265–279 (2004).Article 

Google Scholar 
Irons, D. B. Foraging area fidelity of individual seabirds in relation to tidal cycles and flock feeding. Ecology 79, 647–655 (1998).Article 

Google Scholar 
Piper, W. H. Making habitat selection more “familiar”: a review. Behav. Ecol. Sociobiol. 65, 1329–1351 (2011).Article 

Google Scholar 
Davoren, G. K., Montevecchi, W. A. & Anderson, J. T. Search strategies of a pursuit‐diving marine bird and the persistence of prey patches. Ecol. Monogr. 73, 463–481 (2003).Article 

Google Scholar 
Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).Article 

Google Scholar 
Dall, S. R. X., Bell, A. M., Bolnick, D. I. & Ratnieks, F. L. An evolutionary ecology of individual differences. Ecol. Lett. 15, 1189–1198 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
McAllister, N. M. Avian fauna from the Yuquot excavation in The Yuquot Project, Volume 2 (eds. Folan, W. J. & Dewhirst, J.) 103–174 (National Historic Parks and Sites Branch, 1980).Drucker, P. I. The Northern and Central Nootkan tribes. Bureau of American Ethnology Bulletin 144, 1–480 (1951).
Google Scholar 
Lepofsky, D. & Caldwell, M. Indigenous marine resource management on the Northwest Coast of North America. Ecol. Process 2, 12 (2013).Article 

Google Scholar 
Dewhirst, J. The Indigenous Archaeology of Yuquout, a Nootkan Outside Village (National Historic Parks and Sites Branch, 1980).Longin, R. New method of collagen extraction for radiocarbon dating. Nature 230, 241–242 (1971).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Guiry, E. J. & Hunt, B. P. V. Integrating fish scale and bone isotopic compositions for ‘deep time’ retrospective studies. Mar. Environ. Res. 160, 104982 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hobson, K. A., Atwell, L. & Wassenaar, L. I. Influence of drinking water and diet on the stable-hydrogen isotope ratios of animal tissues. Proc. Natl Acad. Sci. 96, 8003–8006 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Qi, H., Coplen, T. B., Geilmann, H., Brand, W. A. & Böhlke, J. K. Two new organic reference materials for δ13C and δ15N measurements and a new value for the δ13C of NBS 22 oil. Rapid Commun. Mass Spectrom 17, 2483–2487 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Qi, H. et al. A new organic reference material, l-glutamic acid, USGS41a, for δ13C and δ15N measurements − a replacement for USGS41. Rapid Commun. Mass Spectrom 30, 859–866 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Szpak, P., Metcalfe, J. Z. & Macdonald, R. A. Best practices for calibrating and reporting stable isotope measurements in archaeology. J. Archaeol. Sci. Rep 13, 609–616 (2017).
Google Scholar 
Hammer, Ø., Haper, A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 4 (2001).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).RStudio Team. RStudio: Integrated Development for R (RStudio, PBC, 2019). More