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    The bifidobacterial distribution in the microbiome of captive primates reflects parvorder and feed specialization of the host

    1.Arbour, J. H. & Santana, S. E. A major shift in diversification rate helps explain macroevolutionary patterns in primate species diversity. Evolution 71, 1600–1613 (2017).PubMed 
    Article 

    Google Scholar 
    2.Groves, C. Primates (Taxonomy) in The International Encyclopedia of Primatology (ed Augustin Fuentes) (John Wiley & Sons, Inc., 2016).3.Cotton, A., Clark, F., Boubli, J. & Schwitzer, C. IUCN red list of threatened primate species in An Introduction to Primate Conservation 31–18 (Oxford University Press, 2016).
    Google Scholar 
    4.Stumpf, R. M. et al. Microbiomes, metagenomics, and primate conservation: New strategies, tools, and applications. Biol. Conserv. 199, 56–66 (2016).Article 

    Google Scholar 
    5.West, A. G. et al. The microbiome in threatened species conservation. Biol. Conserv. 229, 85–98 (2019).Article 

    Google Scholar 
    6.Cunningham, A. A., Daszak, P. & Wood, J. L. N. One Health, emerging infectious diseases and wildlife: two decades of progress?. Philos. Trans. R. Soc. B: Biol. Sci. 372, 20160167 (2017).Article 

    Google Scholar 
    7.Ramey, A. M. & Ahlstrom, C. A. Antibiotic resistant bacteria in wildlife: Perspectives on trends, acquisition and dissemination, data gaps, and future directions. J. Wildl. Dis. 56, 1–15 (2020).PubMed 
    Article 

    Google Scholar 
    8.Clayton, J. B. et al. Captivity humanizes the primate microbiome. Proc. Natl. Acad. Sci. 113, 10376–10381 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Hale, V. L. et al. Gut microbiota in wild and captive Guizhou snub-nosed monkeys. Rhinopithecus brelichi. Am. J. Primatol. 81, e22989 (2019).CAS 
    PubMed 

    Google Scholar 
    10.Kriss, M., Hazleton, K. Z., Nusbacher, N. M., Martin, C. G. & Lozupone, C. A. Low diversity gut microbiota dysbiosis: drivers, functional implications and recovery. Curr. Opin. Microbiol. 44, 34–40 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Mahnert, A. et al. Man-made microbial resistances in built environments. Nat. Commun. 10, 1–12 (2019).CAS 
    Article 

    Google Scholar 
    12.Amato, K. R. et al. Using the gut microbiota as a novel tool for examining colobine primate GI health. Glob. Ecol. Conserv. 7, 225–237 (2016).Article 

    Google Scholar 
    13.Zhu, H. et al. Diarrhea-associated intestinal microbiota in captive Sichuan golden snub-nosed monkeys (Rhinopithecus roxellana). Microbes Environ. ME17163 (2018).14.Campbell, T. P. et al. The microbiome and resistome of chimpanzees, gorillas, and humans across host lifestyle and geography. ISME J. 14, 1584–1599 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Buzzard, P. J. Ecological partitioning of Cercopithecus campbelli, C. petaurista, and C. diana in the Taï Forest. Int. J. Primatol. 27, 529–558 (2006).Article 

    Google Scholar 
    16.Chapman, C. A. et al. The guenons: diversity and adaptation in African monkeys. 325–350 (Springer, 2004).17.Krishnadas, M., Chandrasekhara, K. & Kumar, A. The response of the frugivorous lion-tailed macaque (Macaca silenus) to a period of fruit scarcity. Am. J. Primatol. 73, 1250–1260 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Swedell, L., Hailemeskel, G. & Schreier, A. Composition and seasonality of diet in wild hamadryas baboons: preliminary findings from Filoha. Folia Primatol. 79, 476–490 (2008).Article 

    Google Scholar 
    19.Basabose, A. K. Diet composition of chimpanzees inhabiting the montane forest of Kahuzi, Democratic Republic of Congo. Am. J. Primatol. 58, 1–21 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.McLennan, M. R. & Ganzhorn, J. U. Nutritional characteristics of wild and cultivated foods for chimpanzees (Pan troglodytes) in agricultural landscapes. Int. J. Primatol. 38, 122–150 (2017).Article 

    Google Scholar 
    21.Newton-Fisher, N. E. The diet of chimpanzees in the Budongo Forest Reserve Uganda. Afr. J. Ecol. 37, 344–354 (1999).Article 

    Google Scholar 
    22.Bach, T. H., Chen, J., Hoang, M. D., Beng, K. C. & Nguyen, V. T. Feeding behavior and activity budget of the southern yellow-cheeked crested gibbons (Nomascus gabriellae) in a lowland tropical forest. Am. J. Primatol. 79, e22667 (2017).Article 

    Google Scholar 
    23.Fan, P.-F., Fei, H.-L., Scott, M. B., Zhang, W. & Ma, C.-Y. Habitat and food choice of the critically endangered cao vit gibbon (Nomascus nasutus) in China: implications for conservation. Biol. Conserv. 144, 2247–2254 (2011).Article 

    Google Scholar 
    24.Fan, P. F., Fei, H. L. & Ma, C. Y. Behavioral responses of cao vit gibbon (Nomascus nasutus) to variations in food abundance and temperature in Bangliang, Jingxi China. Am. J. Primatol. 74, 632–641 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.McConkey, K. R., Ario, A., Aldy, F. & Chivers, D. J. Influence of forest seasonality on gibbon food choice in the rain forests of Barito Ulu Central Kalimantan. Int. J. Primatol. 24, 19–32 (2003).Article 

    Google Scholar 
    26.Amora, T. D., BeltrÃO-Mendes, R. & Ferrari, S. F. Use of alternative plant resources by common marmosets (Callithrix jacchus) in the semi-arid Caatinga scrub forests of northeastern Brazil. Am. J. Primatol. 75, 333–341 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Dietz, J. M., Peres, C. A. & Pinder, L. Foraging ecology and use of space in wild golden lion tamarins (Leontopithecus rosalia). Am. J. Primatol. 41, 289–305 (1997).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Garber, P. A. Feeding ecology and behaviour of the genus Saguinus. Marmosets and tamarins: systematics behaviour and ecology (1993).29.Heymann, E. W., Knogge, C. & Tirado Herrera, E. R. Vertebrate predation by sympatric tamarins, Saguinus mystax and Saguinus fuscicollis. Am. J. Primatol. 51, 153–158 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Porter, L. M. Dietary differences among sympatric Callitrichinae in northern Bolivia: Callimico goeldii, Saguinus fuscicollis and S. labiatus. Int. J. Primatol. 22, 961–992 (2001).Article 

    Google Scholar 
    31.Anapol, F. & Lee, S. Morphological adaptation to diet in platyrrhine primates. Am. J. Phys. Anthropol. 94, 239–261 (1994).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Nash, L. T. Dietary, behavioral, and morphological aspects of gummivory in primates. Am. J. Phys. Anthropol. 29, 113–137 (1986).Article 

    Google Scholar 
    33.Abreu, F., De la Fuente, M. F. C., Schiel, N. & Souto, A. Feeding ecology and behavioral adjustments: flexibility of a small neotropical primate (Callithrix jacchus) to survive in a semiarid environment. Mammal Res. 61, 221–229 (2016).Article 

    Google Scholar 
    34.Cunha, A. A., Vieira, M. V. & Grelle, C. E. V. Preliminary observations on habitat, support use and diet in two non-native primates in an urban Atlantic forest fragment: the capuchin monkey (Cebus sp.) and the common marmoset (Callithrix jacchus) in the Tijuca forest Rio de Janeiro. Urban Ecosyst. 9, 351–359 (2006).Article 

    Google Scholar 
    35.Passamani, M. & Rylands, A. B. Feeding behavior of Geoffroy’s marmoset (Callithrix geoffroyi) in an Atlantic forest fragment of south-eastern Brazil. Primates 41, 27–38 (2000).PubMed 
    Article 

    Google Scholar 
    36.Veracini, C. Habitat use and ranging behavior of the silvery marmoset (Mico argentatus) at Caxiuanã National Forest (eastern Brazilian Amazonia) in The smallest anthropoids 221–240 (Springer, 2009).37.Yépez, P., De La Torre, S. & Snowdon, C. T. Interpopulation differences in exudate feeding of pygmy marmosets in Ecuadorian Amazonia. Am. J. Primatol. 66, 145–158 (2005).PubMed 
    Article 

    Google Scholar 
    38.Hale, V. L. et al. Diet versus phylogeny: a comparison of gut microbiota in captive colobine monkey species. Microb. Ecol. 75, 515–527 (2018).PubMed 
    Article 

    Google Scholar 
    39.Amato, K. R. et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb. Ecol. 69, 434–443 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    40.Frankel, J. S., Mallott, E. K., Hopper, L. M., Ross, S. R. & Amato, K. R. The effect of captivity on the primate gut microbiome varies with host dietary niche. Am. J. Primatol. 81, e23061 (2019).PubMed 
    Article 

    Google Scholar 
    41.McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Lugli, G. A. et al. Evolutionary development and co‐phylogeny of primate‐associated bifidobacteria. Environ. Microbiol. (2020).43.Milani, C. et al. Unveiling bifidobacterial biogeography across the mammalian branch of the tree of life. ISME J. 11, 2834–2847 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Lugli, G. A. et al. Comparative genomic and phylogenomic analyses of the Bifidobacteriaceae family. BMC Genom. 18, 568 (2017).Article 
    CAS 

    Google Scholar 
    45.Pokusaeva, K., Fitzgerald, G. F. & van Sinderen, D. Carbohydrate metabolism in Bifidobacteria. Genes Nutr. 6, 285–306 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Orkin, J. D. et al. Seasonality of the gut microbiota of free-ranging white-faced capuchins in a tropical dry forest. ISME J. 13, 183–196 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Neuzil-Bunesova, V. et al. Five novel bifidobacterial species isolated from faeces of primates in two Czech zoos: Bifidobacterium erythrocebi sp. nov., Bifidobacterium moraviense sp. nov., Bifidobacterium oedipodis sp. nov., Bifidobacterium olomucense sp. nov. and Bifidobacterium panos sp. nov. Int. J. Syst. Evol. Microbiol. (2020).49.Duranti, S. et al. Characterization of the phylogenetic diversity of two novel species belonging to the genus Bifidobacterium: Bifidobacterium cebidarum sp. Nov. and Bifidobacterium leontopitheci sp. nov.. Int. J. Syst. Evol. Microbiol. 70, 2288–2297 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    50.Modesto, M. et al. Bifidobacterium primatium sp. nov., Bifidobacterium scaligerum sp. nov., Bifidobacterium felsineum sp. nov. and Bifidobacterium simiarum sp. nov.: Four novel taxa isolated from the faeces of the cotton top tamarin (Saguinus oedipus) and the emperor tamarin (Saguinus imperator). Syst. Appl. Microbiol. (2018).51.Neuzil-Bunesova, V. et al. Bifidobacterium canis sp nov a novel member of the Bifidobacterium pseudolongum phylogenetic group isolated from faeces of a dog (Canis lupus f. familiaris). Int. J. Syst. Evol. Microbiol. 70, 5040–5047 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Vlková, E. et al. A new medium containing mupirocin, acetic acid, and norfloxacin for the selective cultivation of bifidobacteria. Anaerobe 34, 27–33 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    53.Carding, S., Verbeke, K., Vipond, D. T., Corfe, B. M. & Owen, L. J. Dysbiosis of the gut microbiota in disease. Microb. Ecol. Health Dis. 26, 26191 (2015).
    Google Scholar 
    54.WagnerMackenzie, B. et al. Bacterial community collapse: a meta-analysis of the sinonasal microbiota in chronic rhinosinusitis. Environ. Microbiol. 19, 381–392 (2017).CAS 
    Article 

    Google Scholar 
    55.Arboleya, S., Watkins, C., Stanton, C. & Ross, R. P. Gut bifidobacteria populations in human health and aging. Front. Microbiol. 7 (2016).56.Binda, C. et al. Actinobacteria: a relevant minority for the maintenance of gut homeostasis. Dig. Liver Dis. 50, 421–428 (2018).MathSciNet 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Tojo, R. et al. Intestinal microbiota in health and disease: role of bifidobacteria in gut homeostasis. World J. Gastroenterol. 20, 15163 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Rodriguez, C. I. & Martiny, J. B. H. Evolutionary relationships among bifidobacteria and their hosts and environments. BMC Genom. 21, 1–12 (2020).Article 

    Google Scholar 
    59.Sharma, V., Mobeen, F. & Prakash, T. Exploration of survival traits, probiotic determinants, host interactions, and functional evolution of bifidobacterial genomes using comparative genomics. Genes 9, 477 (2018).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    60.Sun, Z. et al. Comparative genomic analysis of 45 type strains of the genus Bifidobacterium. a snapshot of its genetic diversity and evolution. PLoS One 10, 0117912 (2015).
    Google Scholar 
    61.Frey, J. C. et al. Fecal bacterial diversity in a wild gorilla. Appl. Environ. Microbiol. 72, 3788–3792 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Makovska, M., Modrackova, N., Bolechova, P., Drnkova, B. & Neuzil-Bunesova, V. Antibiotic susceptibility screening of primate-associated Clostridium ventriculi. Anaerobe, 102347 (2021).63.Ushida, K. et al. Draft genome sequences of Sarcina ventriculi strains isolated from wild Japanese macaques in Yakushima Island. Genome announcements 4 (2016).64.Owens, L. A. et al. A Sarcina bacterium linked to lethal disease in sanctuary chimpanzees in Sierra Leone. Nat. Commun. 12, 1–16 (2021).ADS 
    Article 
    CAS 

    Google Scholar 
    65.Vlková, E., Rada, V., Šmehilová, M. & Killer, J. Auto-aggregation and co-aggregation ability in bifidobacteria and clostridia. Folia Microbiol. 53, 263–269 (2008).Article 
    CAS 

    Google Scholar 
    66.Wang, L. et al. Adhesive Bifidobacterium induced changes in cecal microbiome alleviated constipation in mice. Front. Microbiol. 10, 1721 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Wei, Y. et al. Protective effects of bifidobacterial strains against toxigenic Clostridium difficile. Front. Microbiol. 9, 888 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Guittar, J., Shade, A. & Litchman, E. Trait-based community assembly and succession of the infant gut microbiome. Nature Commun. 10, 1–11 (2019).Article 
    CAS 

    Google Scholar 
    69.Moore, R. E. & Townsend, S. D. Temporal development of the infant gut microbiome. Open Biol. 9, 190128 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Korpela, K. et al. Probiotic supplementation restores normal microbiota composition and function in antibiotic-treated and in caesarean-born infants. Microbiome 6, 1–11 (2018).Article 

    Google Scholar 
    71.Timperio, A. M., Gorrasi, S., Zolla, L. & Fenice, M. Evaluation of MALDI-TOF mass spectrometry and MALDI BioTyper in comparison to 16S rDNA sequencing for the identification of bacteria isolated from Arctic sea water. PloS One 12, 0181860 (2017).Article 
    CAS 

    Google Scholar 
    72.Bäckhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    73.Brown, C. J. et al. Comparative genomics of Bifidobacterium species isolated from marmosets and humans. Am. J. Primatol. 81, e983 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Killer, J. et al. Gene encoding the CTP synthetase as an appropriate molecular tool for identification and phylogenetic study of the family Bifidobacteriaceae. MicrobiologyOpen 7, e00579 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    75.Milani, C. et al. Evaluation of bifidobacterial community composition in the human gut by means of a targeted amplicon sequencing (ITS) protocol. FEMS Microbiol. Ecol. 90, 493–503 (2014).CAS 
    PubMed 

    Google Scholar 
    76.Srinivasan, R. et al. Use of 16S rRNA gene for identification of a broad range of clinically relevant bacterial pathogens. PloS One 10, e0117617 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    77.Maiden, M. C. J. et al. MLST revisited: the gene-by-gene approach to bacterial genomics. Nature Rev. Microbiol. 11, 728–736 (2013).CAS 
    Article 

    Google Scholar 
    78.Lugli, G. A. et al. Phylogenetic classification of six novel species belonging to the genus Bifidobacterium comprising Bifidobacterium anseris sp. nov., Bifidobacterium criceti sp. nov., Bifidobacterium imperatoris sp. nov., Bifidobacterium italicum sp. nov., Bifidobacterium margollesii sp. nov. and Bifidobacterium parmae sp. nov. Syst. Appl. Microbiol. 41, 173–183 (2018).PubMed 
    Article 

    Google Scholar 
    79.Malukiewicz, J. et al. The effects of host taxon, hybridization, and environment on the gut microbiome of Callithrix marmosets. BioRxiv, 708255 (2019).80.Amato, K. R. et al. Phylogenetic and ecological factors impact the gut microbiota of two Neotropical primate species. Oecologia 180, 717–733 (2016).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Hernández‐Rodríguez, D., Vásquez‐Aguilar, A. A., Serio‐Silva, J. C., Rebollar, E. A. & Azaola‐Espinosa, A. Molecular detection of Bifidobacterium spp. in faeces of black howler monkeys (Alouatta pigra). J. Med. Primatol. 48, 99–105 (2019).82.Zhu, L. et al. Sex bias in gut microbiome transmission in newly paired marmosets (Callithrix jacchus). Msystems 5, e00910-00919 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    83.Kap, Y. S. et al. Targeted diet modification reduces multiple sclerosis–like disease in adult marmoset monkeys from an outbred colony. J. Immunol. 201, 3229–3243 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    84.Ren, T., Grieneisen, L. E., Alberts, S. C., Archie, E. A. & Wu, M. Development, diet and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ. Microbiol. 18, 1312–1325 (2016).PubMed 
    Article 

    Google Scholar 
    85.Xu, B. et al. Metagenomic analysis of the Rhinopithecus bieti fecal microbiome reveals a broad diversity of bacterial and glycoside hydrolase profiles related to lignocellulose degradation. BMC Genom. 16, 1–11 (2015).Article 
    CAS 

    Google Scholar 
    86.Baumann, P. Biology of bacteriocyte-associated endosymbionts of plant sap-sucking insects. Annu. Rev. Microbiol. 59, 155–189 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    87.Killer, J. et al. Bifidobacterium actinocoloniiforme sp. nov. and Bifidobacterium bohemicum sp. nov., from the bumblebee digestive tract. Int. J. Syst. Evol. Microbiol. 61, 1315–1321 (2011).88.Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    89.Garber, P. A., Mallott, E. K., Porter, L. M. & Gomez, A. The gut microbiome and metabolome of saddleback tamarins (Leontocebus weddelli): Insights into the foraging ecology of a small‐bodied primate. Am. J. Primatol. 81, e23003 (2019).90.Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Clayton, J. B. et al. Associations between nutrition, gut microbiome, and health in a novel nonhuman primate model. Sci. Rep. 8, 1–16 (2018).CAS 
    Article 

    Google Scholar 
    92.Koo, B. S. et al. Idiopathic chronic diarrhea associated with dysbiosis in a captive cynomolgus macaque (Macaca fascicularis). J. Med. Primatol. 49, 56–59 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Krynak, K. L., Burke, D. J., Martin, R. A. & Dennis, P. M. Gut microbiome composition is associated with cardiac disease in zoo-housed western lowland gorillas (Gorilla gorilla gorilla). FEMS Microbiol. Lett. 364 (2017).94.Modrackova, N. et al. Prebiotic potential of natural gums and starch for bifidobacteria of variable origins. Bioact. Carbohydr. Diet. Fibre 20, 100199 (2019).95.McKenzie, V. J., Kueneman, J. G. & Harris, R. N. Probiotics as a tool for disease mitigation in wildlife: insights from food production and medicine. Ann. N. Y. Acad. Sci. 1429, 18–30 (2018).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    96.Hicks, A. L. et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat. Commun. 9, 1–18 (2018).CAS 
    Article 

    Google Scholar 
    97.Hungate, R. E. & Macy, J. The roll-tube method for cultivation of strict anaerobes. Bulletins from the ecological research committee, 123–126 (1973).98.Rada, V. & Petr, J. A new selective medium for the isolation of glucose non-fermenting bifidobacteria from hen caeca. J. Microbiol. Methods 43, 127–132 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    99.Orban, J. I. & Patterson, J. A. Modification of the phosphoketolase assay for rapid identification of bifidobacteria. J. Microbiol. Methods 40, 221–224 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    100.Kim, B. J., Kim, H.-Y., Yun, Y.-J., Kim, B.-J. & Kook, Y.-H. Differentiation of Bifidobacterium species using partial RNA polymerase β-subunit (rpoB) gene sequences. Int. J. Syst. Evol. Microbiol. 60, 2697–2704 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    101.Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. 41 edn 95–98 ([London]: Information Retrieval Ltd., c1979-c2000.).102.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).CAS 
    PubMed 
    PubMed Central 
    Article 

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

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

    Google Scholar 
    105.Shannon, C. E. & Weaver, W. The mathematical theory of information. Urbana: University of Illinois Press 97 (1949).106.Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).ADS 
    Article 

    Google Scholar 
    107.Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    108.fundamental algorithms for scientific computing in Python. Virtanen, P. et al. SciPy 1.0. Nat. Methods 17, 261–272 (2020).Article 
    CAS 

    Google Scholar 
    109.Seabold, S. & Perktold, J. Statsmodels: Econometric and statistical modeling with python in Proceedings of the 9th Python in Science Conference 57 (Austin, TX, 2010).110.MacKinnon, J. G. & White, H. Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. J. Econom. 29, 305–325 (1985).Article 

    Google Scholar  More

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    Population consequences of climate change through effects on functional traits of lentic brown trout in the sub-Arctic

    Sampling and dataThe data consist of gillnet catches of brown trout (N = 5733, caught during 2008–2009) from 21 lakes situated along an altitudinal gradient (30 m above sea level, m.a.s.l.-800 m.a.s.l.) in mid-Norway and Sweden (Fig. 5). The lakes were sampled within three main types of vegetation zonation in the catchment area that ranged from the southern boreal to the alpine zone. The lowland lakes were situated in the southern boreal zone dominated by coniferous woodland and forest, but there were also large areas of alder (Alnus sp.) as well as some broad-leaved deciduous woodland. Average annual and July air temperature are 4–6 and 12–16 °C, respectively46. Middle boreal catchment area is dominated by coniferous woodland, forest and mires. Average annual and July air temperature are, respectively, 2–4 and 8–12°C46. Vegetation around the high altitude lakes were dominated by bilberry (Vaccinium myrtillus), grass heaths and dwarf birch (Betula nana) scrub, with annual and July air temperature of − 2 to 0 and 6–12°C46. The clustering of lakes within vegetations zones can be seen in Fig. 5. The epilimnetic water temperature across a sample of the lakes in the altitudinal gradient in this study seems to be within the general trends in the air temperature47.Figure 5Study lake positions (filled dots) and names. Unfilled large circles connects the different lakes with the most representative weather stations (stars) in the area (in terms of altitude, vegetation zones and landscape). The dashed line constitute the national border between Norway and Sweden. The figure was produced using Adobe illustrator.Full size imageAll lakes were sampled using standardised gillnet series consisting of single mesh nets (25 × 1.5 m) with mesh sizes 12.5, 16, 19.5, 24, 29 and 35 mm47. Three nets were linked together making chains with alternating mesh sizes in order to represent all mesh sizes at different depths in each lake at each sampling. This gillnet series catches brown trout with a slight bias in favour of larger individuals48 that was assumed similar in all lakes. The nets were distributed along the shoreline, and the lakes were fished during summer, with different effort (i.e., number of gillnet series) depending on lake size. Weight per unit catch effort (CPUE) based on total weight of the brown trout catch per 100 m2 gillnet area per night was used as a proxy for biomass density. Since, differences in environmental conditions across lakes cause large variations in body size and hence per capita resource demands, biomass was considered a better measure of population density than number of individuals for among-lake comparisons. Length (total length, mm) and weight (g) at catch were measured for every individual in the full data set. Age, sex, maturation status and back-calculation of length-at-age was undertaken for a randomly selected representative subset (N = 889) of the data. Growth and spawning probability ogives49 were modeled based on this subset. Scale samples and otoliths were taken and used for age determination, of which scales were used primarily, and scales were used for back-calculation of growth50. Distance between the annuli was measured, and a direct proportional relationship between the length of the fish and the scale radius was assumed51. If the scales were difficult to read, which was the case for more slow-growing individuals from the low altitude lakes were the annuli were less distinct, otoliths were used for determining the age. As we did not have complete records of water temperature, area and time specific summer air temperature and precipitation measurements were obtained from an online database (www.eklima.no, Norwegian Metrological Institute). The database contained historical weather data from the closest representative (i.e., corresponding in distance, altitude and operational period) weather stations to the respective lakes (Fig. 5). This resulted in overlapping temperature and precipitation regimes for some of the lakes as there were in total five different weather stations that were most representative within the area containing the 21 lakes. Further, as there was some variation in how complete the different measurements were within years, we also had to calculate the sum of summer precipitation for a shorter period of the summer compared to the average mean air temperature. Both measurements still being good proxies for experienced summer conditions in the bulk of the growth season. The effect of temperature and precipitation was thus derived from the spatio-temporal variation in observations between these five weather stations, where the historic temporal variation corresponds to recorded climate components relevant to years for the back calculated age of the individual fish in the specific lakes (resulting in a total of 29 distinct measurements, see variation in Table 2) Epilimnetic water of lakes usually reflects warming trends in air temperature well, however hypolimnetic temperature variation might not be very correlated to the air temperature. Yet, changes in air temperature might indeed influence the thermal stratification of a lake and thus the environment and conditions for a fish52. There are good reasons to believe that most of the lakes in our study obtained some sort of thermal stratification during the summer season. Nonetheless, we chose not to model air to water temperature for the few measurements of water temperatures we had, and extrapolate this relationship to the full spatio-temporal resolution of the data. The rationale for this was threefold: (1) We were interested in exploring potential effects and relationships of easily available climate components, such as air temperature, simplifying the model concept; (2) we did not have access to detailed data on lake bathymetry so that hypothetical modeled air-to-water relationships would be rather uncertain; (3) we had no detailed information on how the brown trout was distributed in the water column during the summer period in study lakes. However, compared to similar lakes, there are reasons to believe that brown trout mainly feed and stay in the upper six meters of the water column, as well as epibenthic areas with high invertebrate abundances53,54, where both areas often are overlapping and highly influenced by the air temperature.Table 2 Description of candidate variables used in the model selection process determining the most supported model for individual growth of brown trout.Full size tableData analysis and model descriptionsOverall processWe used linear mixed model approaches to parameterize environmental effects on key life history traits for brown trout. Specifically: Length at age was parameterized as function of the environment (e.g., summer temperature, population density, winter NAO and summer precipitation). Spawning probability were modeled as functions of individual length and age. We also allowed either the age effect or length effect on spawning probability to vary with temperature or summer precipitation. Individual fecundity (number of eggs produced) was predicted as a function of length and spawning probability. Annual survival estimates from age 1 and up was accessed using catch curve analysis, while first year survival was estimated based on a stock-recruitment function. The estimated parameters were utilized to feed an age structured matrix projection model23, enabling long-term population viability projections in an changing environment (see overview in Fig. 6). Although there are several choices of population models that might be utilized for inferring the population dynamics, such as IBMs55 and IPMs56, the age structured matrix model was deemed especially suited to model our systems as they are highly seasonal (with very reduced growth during winter) and thus producing a clear age structure in the data. Further description of the various modeling approaches are described below. All statistical analyses was done in R57.Figure 6A schematic overview of the processes involved in our model-setup. Red lines indicate drivers and connections acting on individual life history traits, blue lines indicates traits driving the population model and green lines indicates links to climate variables. In short, existing area and time specific climate data on summer precipitation (Prec) and mean summer air temperature (Temp), as well as time specific data on winter NAO-index (recorded NAO values during December, January, February and March, NAO.DJFM), were used to parameterize models for length at age 1 and length at age  > 1, as well as spawning probability at age. Length at age 1 was allowed to affect length at age  > 1, and in the simulations achieved length at age  > 1 was also influenced by the achieved length the previous year (L*). Length at age and spawning probability, both defined by climate variables, interacted in defining how many eggs a female was likely to produce (i.e. fecundity). Survival from eggs to small juvenile fish was based on a stock-recruitment relationship, where the stock was defined by the results from the population model (expected number of fish). Expected number of fish across all ages was also allowed to affect length at age  > 1. The model parameters was used to simulate long term population dynamics, where we also varied expected temperature change scenarios (steadily increasing mean temperatures and temperature variation, respectively, as well as a combination of the two latter scenarios). The populations long term rate of increase (λ) was inferred using the age structured population matrix model.Full size imageSize at ageData inspections prior to model development showed length at age to be surprisingly linear within the size and age distribution in our data (i.e. no obvious signs of asymptotic growth for fish in any of the sampled lakes). Length (L) was thus explored using a linear mixed effects model approach with the lme4-package58. Denoted, length for individual j in population i (Lij) could thus be expressed as:$${L}_{ij}={sumlimits_{k=1}^{p}}{chi }_{ijk}{beta }_{k}$$Here, β = (β1, …, βp)T is px1 vector (one column matrix) of unknown regression parameters, χiT = (χi1, …, χip) ∈ ℝp is the explanatory variables of interest (k + p  1, age was always included as a variable, and we also tested models including an effect of CPUE and first year growth on subsequent growth trajectories. Multiple candidate models where the different environmental effects were allowed to vary with age were constructed (Supplementary information S1). Population ID and individual ID were included as nested random effects in all candidate models exploring size at age  > 1, and population ID was included as a random effect for the models exploring size at age 1. The most supported models were selected based on AIC-values59. During the population simulation the variation in the predictions attributed to the random effect(s) was treated as random noise, and not explicitly included in the simulations.Spawning probabilityBrown trout is an iteroparous species, however under normal food conditions and harsh winters in Norway it might not spawn every year following maturity. Accordingly, we modelled likelihood of spawning at age, derived from the number of female individuals that was going to spawn the following autumn, rather than probability of maturation at age. Aging effects on spawning probability was included in the modelling as skipped-spawning individuals (i.e., mature females that skip spawning episodes, sensu Rideout and Tomkiewicz 60) were coded as non-spawners in the analysis. Probability of spawning (P) was calculated based on a maturation-ogives approach61, utilizing generalized linear mixed effects models in the lme4-package58. Binomial models as two-dimensional ogives, o(A, L) were considered in the model selection. Here, A and L represent age and length, respectively. In addition, we also explored how these ogives might change due to either a temperature effect, summer precipitation effect, or a measure of fish abundance (CPUE) including either as an additive effect in some candidate models (see Supplementary information S2). Population ID was always included as a random effect. In general, the probability of spawning could thus be described as:$${mathrm{Pr}left(spawningright)}_{ij}={beta }_{0i}+{beta }_{1i}{A}_{ij}+{beta }_{2i}{L}_{ij}+{beta }_{3i}{A}_{ij}{L}_{ij}+{beta }_{4i}{x}_{1i}+{a}_{i}+{varepsilon }_{ij}$$
    where βs represent coefficients under estimation, Aij = age of individual j in population i, L = individual length, x1 represent a lake-specific environmental variable (if present in the candidate model, either summer temperature, CPUE or precipitation), ai is the estimated random lake-specific intercept and εij is the random residual variation assumed normally distributed on logit scale. The most supported model was selected based on AICc-values59.FecundityFemale fecundity (i.e., number of eggs per female) was predicted as a function of female length (mm) and two constants based upon published values for brown trout from Norway (F = e log(l)*2.21–6.15)62 multiplied by the probability of spawning (P) at size and age.SurvivalAnnual survival rates (s) for fish age ≥ 1 were based on estimations from catch-curve slopes utilizing the Chapman-Robson function in the FSA-package63. The survival was estimated based on descending catch curves, i.e., where numbers of caught individuals decreased as a function of age in the catch. Based on this slope we can derive an instantaneous mortality rate (Z), and from this the annual survival rate could be estimated from S = e-Z. Due to a restricted number of populations available for survival rates, the survival was estimated across all population. As it is unlikely that S would be constant across all age classes we choose to make age specific survival rates, Sa, where the S1 (survival from age one to age two) was reduced, and S3-5 was slightly increased whereas all other Sa = S. The respective reduction and increase are described more in detail below. Survival rates for age 0–1, S0, was based on a stock-recruitment function (see further description under “Climate scenarios, calibration and population projections”).The projection matrixPopulation projections were derived utilizing an age-structured matrix population model23 in the popbio-package in R64. Changes in the age structure and abundance of brown trout was modelled from Nt+1 = K(E,N,t)Nt or rather:$${left[begin{array}{c}{N}_{1}\ {N}_{2}\ vdots \ vdots \ {N}_{{a}_{max}}end{array}right]}_{t+1}=left[begin{array}{ccccc}{f}_{1}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)& {f}_{2}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)& cdots & cdots & {f}_{{a}_{max}}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)\ {s}_{a}& 0& cdots & cdots & 0\ 0& {s}_{a}& cdots & cdots & 0\ vdots & vdots & vdots & vdots & vdots \ 0& 0& 0& {s}_{a}& 0end{array}right]times {left[begin{array}{c}{N}_{1}\ {N}_{2}\ vdots \ vdots \ {N}_{{a}_{max}}end{array}right]}_{t}$$
    where Nt is the abundance of brown trout across all age classes a = 1,…, amax at year t. Census time is chosen so that reproduction occurs at the beginning of each annual season. fa is the fecundity at age a (i.e., the number of offspring produced per individual of age a during a year). More specifically, f varies according to f(L,P,N), where variations in L (length) and P (probability to spawn) in turn is defined by climate variables and the number of individuals N. s is a constant and represent the survival probability of individuals from age a to age a + 1, and amax is the maximum age considered in the model. amax was set to 10 years in the simulations, as was also was the age of the oldest fish in the aged subset of the data (see frequency table in Supplementary information S2). Although varying between systems, the maximum age observed and simulated also corresponds to expected maximum age found in other systems in Norway65. S0 is a function of E, the numbers of eggs laid, where the relationship is determined by a stock-recruitment function.Consequently, K(E,N,t), the Leslie matrix, is a function of N and E. In each time step, the survival of individuals in age class amax is 0, whereas individuals at all other ages spawn and experience mortality as defined above. From the Leslie matrix K, we can infer the population’s long-term rate of increase, λ, from the dominant eigenvector of the matrix23.Climate scenarios, calibration and population projectionsTo explore the population effects of changes in summer air temperature or winter conditions we simulated different 100-years climate-change scenarios for a single lake, which included variations the climate variables in focus. The first scenario represented a status quo setting. Here, annual average summer air temperatures were randomly drawn from a normal distribution with mean and standard deviation from observed summer air temperatures from 1998–2009 in the study area. The second climate scenario randomly assigned temperatures as in scenario one, as well as allowing for more and more fluctuating annual summer temperatures as time progressed. This was done by adding a random variable t (~ N(0,0.03) times the number of the specific year (i.e., 1–100) in the 100-years climate change scenario. The third climate scenario, drew annual summer temperatures as in the first scenario, but included an increase in the average air summer temperature by 0.04 °C each year (i.e., 4 °C in total for the 100-year-scenario which is close to the expected mean increase in regional temperature following the regionally down-scaled RCP8.5 IPCC scenario66). The fourth climate scenario included an average summer temperature increase of 0.02 °C each year (close to the expected average temperature increase following the regionally down-scaled RCP4.5 IPCC scenario66), as well as allowing for more and more fluctuating annual summer temperatures as time progressed (as in scenario two). For all climate scenarios above, annual winter NAO-values was randomly drawn from a uniform distribution between − 1.5 and 1.5.We also simulated a second set of climate change scenarios, where summer temperatures were as described in the four scenarios above, however in all these scenarios we also included a trend of higher winter NAO values (meaning a general trend of warmer winters with more precipitation/snow in the study area, as predicted by the down scaled climate scenarios66). This was done by letting annual NAO-values be drawn from a random normal distribution with mean = 0.5, and standard deviation of 0.5.During the calibration process for the simulations, we altered the age specific survival estimates S1 and S3-5 so that average lambdas for the status quo climate scenario was relatively stable and close to 1 (i.e. no large changes in population size) based on 100 iterations of a 100 year-climate scenario. Specifically, S1 = S*0.6 and S3-5 = S*1.2, which is also assumed to be within the realistic range of survival rates for the specific age classes in the focal populations. S0 was derived from a stock recruitment function, and was thus allowed to vary as a function of density in the population. Specifically, from the total egg number (Et) at year t and the number of one-year olds at year t + 1 (N1,t+1) the stock-recruitment function could be estimated by fitting a Shepherd function67:$${N}_{1,t+1}=frac{a{E}_{t}}{{left(1+b{E}_{t}right)}^{c}}$$
    where a = 0.04, b = 0.0000003 and c = 3.5. E is number of eggs deposited during t-1 spawning season, estimated as the total fecundity. The estimated N1,t+1 was used to estimate first-year survival (s0) from:$${s}_{0,t}=mathrm{ln}left({E}_{t-1}right)-mathrm{ln}left({N}_{1,t}right)$$All 100-years scenarios were simulated with 100 iterations to extract the variation in the expected population projections. CPUE in the simulations was included as a dynamic variable in the growth model, recalculated through the matrix projection model for each time step, i.e. year. Length at age, spawning probability and fecundity was predicted for each time step (i.e. pr year) as described above. The spawning probability did however not vary annually according to changes in the environment but was predicted according to the mean values of the environmental variables across all years the climate scenario. However, for climate scenarios with increasing mean temperature over time, the expected spawning probability was a function of the gradual mean temperature increase. Thus, by allowing the spawning probability reaction norm gradually to follow changes in the temperature, as predicted from the spawning model, we allowed the populations to gradually adapt the reaction norm to the respective changes. More

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    Madrepora oculata forms large frameworks in hypoxic waters off Angola (SE Atlantic)

    1.Roberts, J. M., Wheeler, A. J., Freiwald, A. & Cairns, S. D. Cold-Water Corals. The Biology and Geology of Deep-Sea Coral Habitats. (Cambridge University Press, 2009).2.Davies, A. J. & Guinotte, J. M. Global habitat suitability for framework-forming cold-water corals. Plos One 6, e18483 (2011).3.Morato, T. et al. Climate-induced changes in the suitable habitat of cold-water corals and commercially important deep-sea fishes in the North Atlantic. Glob. Chang. Biol. 26, 2181–2202. https://doi.org/10.1111/gcb.14996 (2020).ADS 
    Article 
    PubMed Central 

    Google Scholar 
    4.Arnaud-Haond, S. et al. Two “pillars” of cold-water coral reefs along Atlantic European margins: Prevalent association of Madrepora oculata with Lophelia pertusa, from reef to colony scale. Deep-Sea Res. Pt. II(145), 110–119 (2017).Article 

    Google Scholar 
    5.Buhl-Mortensen, L., Olafsdottir, S. H., Buhl-Mortensen, P., Burgos, J. M. & Ragnarsson, S. A. Distribution of nine cold-water coral species (Scleractinia and Gorgonacea) in the cold temperate North Atlantic: Effects of bathymetry and hydrography. Hydrobiologia 759, 39–61. https://doi.org/10.1007/s10750-014-2116-x (2015).CAS 
    Article 

    Google Scholar 
    6.Gori, A. et al. Bathymetrical distribution and size structure of cold-water coral populations in the Cap de Creus and Lacaze-Duthiers canyons (northwestern Mediterranean). Biogeosciences 10, 2049–2060. https://doi.org/10.5194/bg-10-2049-2013 (2013).ADS 
    Article 

    Google Scholar 
    7.Orejas, C. et al. Cold-water corals in the Cap de Creus canyon (north-western Mediterranean): Spatial distribution, density and anthropogenic impact. Mar. Ecol. Prog. Ser. 397, 37–51 (2009).ADS 
    Article 

    Google Scholar 
    8.Buhl-Mortensen, P. Coral reefs in the Southern Barents Sea: Habitat description and the effects of bottom fishing. Mar. Biol. Res. 13, 1027–1040. https://doi.org/10.1080/17451000.2017.1331040 (2017).Article 

    Google Scholar 
    9.Cairns, S. Antarctic and subantarctic Scleractinia. Antarctic Res. Ser. 34. https://doi.org/10.1029/AR034p0001 (1983).10.Cairns, S. D. & Zibrowius, H. Cnidaria Anthozoa: Azooxanthellate Scleractinia from the Philippine and Indonesian regions. Mém. Mus. Natl. Hist. Nat. 172, 27–243 (1997).
    Google Scholar 
    11.Tracey, D., Rowden, A., Mackay, K. & Compton, T. Habitat-forming cold-water corals show affinity for seamounts in the New Zealand region. Mar. Ecol. Prog. Ser. 430, 1–22. https://doi.org/10.3354/meps09164 (2011).ADS 
    Article 

    Google Scholar 
    12.Auscavitch, S. R. et al. Oceanographic drivers of deep-sea coral species distribution and community assembly on seamounts, islands, atolls, and reefs within the Phoenix Islands protected area. Front. Mar. Sci. 7. https://doi.org/10.3389/fmars.2020.00042 (2020).13.Angeletti, L., Castellan, G., Montagna, P., Remia, A. & Taviani, M. “The Corsica channel cold-water coral province” (Mediterranean Sea). Front. Mar. Sci. 7. https://doi.org/10.3389/fmars.2020.00661 (2020).14.Chimienti, G., Bo, M., Taviani, M. & Mastrototaro, F. in Mediterranean Cold-Water Corals: Past, Present and Future, Springer Series: Coral Reefs of the World (eds. Covadonga Orejas Saco del Valle & C. Jiménez) 213–243 (Springer, 2019).15.Corbera, G. et al. Ecological characterisation of a Mediterranean cold-water coral reef: Cabliers Coral Mound Province (Alboran Sea, western Mediterranean). Prog. Oceanogr. 175, 245–262. https://doi.org/10.1016/j.pocean.2019.04.010 (2019).ADS 
    Article 

    Google Scholar 
    16.Freiwald, A. et al. The White Coral Community in the Central Mediterranean Sea revealed by ROV surveys. Oceanography 22, 58–74 (2009).Article 

    Google Scholar 
    17.Fabri, M. C. et al. Megafauna of vulnerable marine ecosystems in French Mediterranean submarine canyons: Spatial distribution and anthropogenic impacts. Deep-Sea Res. Pt. II(104), 184–207. https://doi.org/10.1016/j.dsr2.2013.06.016 (2014).Article 

    Google Scholar 
    18.Brooke, S. & Ross, S. W. First observations of the cold-water coral Lophelia pertusa in mid-Atlantic canyons of the USA. Deep-Sea Res. Pt. II(104), 245–251 (2014).Article 

    Google Scholar 
    19.Cordes, E. E. et al. Coral communities of the deep Gulf of Mexico. Deep-Sea Res. Pt. II(55), 777–787 (2008).Article 

    Google Scholar 
    20.Frederiksen, R., Jensen, A. & Westerberg, H. The distribution of scleratinian coral Lophelia pertusa around the Faroe Islands and the relation to intertidal mixing. Sarsia 77, 157–171 (1992).Article 

    Google Scholar 
    21.Hebbeln, D. et al. Environmental forcing of the Campeche cold-water coral province, southern Gulf of Mexico. Biogeosciences 11, 1799–1815. https://doi.org/10.5194/bg-11-1799-2014 (2014).ADS 
    Article 

    Google Scholar 
    22.Wienberg, C. et al. Franken Mound: Facies and biocoenoses on a newly-discovered “carbonate mound” on the western Rockall Bank, NE Atlantic. Facies 54, 1–24. https://doi.org/10.1007/s10347-007-0118-0 (2008).Article 

    Google Scholar 
    23.Purser, A. et al. Local variation in the distribution of benthic megafauna species associated with cold-water coral reefs on the Norwegian margin. Cont. Shelf Res. 54, 37–51. https://doi.org/10.1016/j.csr.2012.12.013 (2013).ADS 
    Article 

    Google Scholar 
    24.Fanelli, E. et al. Cold-water coral Madrepora oculata in the eastern Ligurian Sea (NW Mediterranean): Historical and recent findings. Aquat. Conserv. 27, 965–975. https://doi.org/10.1002/aqc.2751 (2017).Article 

    Google Scholar 
    25.Naumann, M. S., Orejas, C. & Ferrier-Pagès, C. Species-specific physiological response by the cold-water corals Lophelia pertusa and Madrepora oculata to variations within their natural temperature range. Deep-Sea Res. Pt. II(99), 36–41. https://doi.org/10.1016/j.dsr2.2013.05.025 (2014).CAS 
    Article 

    Google Scholar 
    26.Movilla, J. et al. Resistance of two mediterranean cold-water coral species to low-pH conditions. Water 6, 59–67 (2014).ADS 
    Article 

    Google Scholar 
    27.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 oxgen change. J. Exp. Mar. Biol. Ecol. 349, 205–214 (2007).CAS 
    Article 

    Google Scholar 
    28.Lunden, J. J., McNicholl, C. G., Sears, C. R., Morrison, C. L. & Cordes, E. E. Acute survivorship of the deep-sea coral Lophelia pertusa from the Gulf of Mexico under acidification, warming, and deoxygenation. Front. Mar. Sci. 1. https://doi.org/10.3389/fmars.2014.00078 (2014).29.Ramos, A., Sanz, J. L., Ramil, F., Agudo, L. M. & Presas-Navarro, C. in Deep-Sea Ecosystems Off Mauritania: Research of Marine Biodiversity and Habitats in the Northwest African Margin (eds. Ramos, A., Ramil, F., & Sanz, J.L.) 481–525 (Springer, 2017).30.Wienberg, C. et al. The giant Mauritanian cold-water coral mound province: Oxygen control on coral mound formation. Quat. Sci. Rev. 185, 135–152. https://doi.org/10.1016/j.quascirev.2018.02.012 (2018).ADS 
    Article 

    Google Scholar 
    31.Hanz, U. et al. Environmental factors influencing cold-water coral ecosystems in the oxygen minimum zones on the Angolan and Namibian margins. Biogeosciences 16, 4337–4356 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    32.Hebbeln, D. et al. Cold-water coral reefs thriving under hypoxia. Coral Reefs 39, 853–859. https://doi.org/10.1007/s00338-020-01934-6 (2020).Article 

    Google Scholar 
    33.Montero-Serrano, J.-C. et al. Decadal changes in the mid-depth water mass dynamic of the Northeastern Atlantic margin (Bay of Biscay). Earth Planet. Sci. Lett. 364, 134–144. https://doi.org/10.1016/j.epsl.2013.01.012 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Orejas, C., Gori, A. & Gili, J. M. Growth rates of live Lophelia pertusa and Madrepora oculata cold-water coral species maintained in aquaria. Coral Reefs 27, 255 (2008).ADS 
    Article 

    Google Scholar 
    35.Sabatier, P. et al. 210Pb-226Ra chronology reveals rapid growth rate of Madrepora oculata and Lophelia pertusa on world’s largest cold-water coral reef. Biogeosciences 9, 1253–1265. https://doi.org/10.5194/bg-9-1253-2012 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    36.Sweetman, A. et al. Major impacts of climate change on deep-sea benthic ecosystems. Elementa-Sci. Anthrop. 5, 4. https://doi.org/10.1525/elementa.203 (2017).Article 

    Google Scholar 
    37.Lexerød, N. L. Recruitment models for different tree species in Norway. For. Ecol. Manag. 206, 91–108. https://doi.org/10.1016/j.foreco.2004.11.001 (2005).Article 

    Google Scholar 
    38.Georgian, S. et al. Biogeographic variability in the physiological response of the cold-water coral Lophelia pertusa to ocean acidification. Mar. Ecol. 37. https://doi.org/10.1111/maec.12373 (2016).39.Tamborrino, L. et al. Mid-Holocene extinction of cold-water corals on the Namibian shelf steered by the Benguela oxygen minimum zone. Geology 47, 1185–1188. https://doi.org/10.1130/g46672.1 (2019).ADS 
    Article 

    Google Scholar 
    40.Büscher, J., Form, A. & Riebesell, U. Interactive effects of ocean acidification and warming on growth, fitness and survival of the cold-water coral Lophelia pertusa under different food availabilities. Front. Mar. Sci. 4. https://doi.org/10.3389/fmars.2017.00101 (2017).41.Connolly, S., Lopez-Yglesias, M. & Anthony, K. Food availability promotes rapid recovery from thermal stress in a scleractinian coral. Coral Reefs 31. https://doi.org/10.1007/s00338-012-0925-9 (2012).42.Middelburg, J. J. et al. Discovery of symbiotic nitrogen fixation and chemoautotrophy in cold-water corals. Sci. Rep. 5, 17962. https://doi.org/10.1038/srep17962 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Wienberg, C. & Titschack, J. in Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds. Rossi, S., Bramanti, L., Gori, A., & del Valle, C.O.S.) 699–732 (Springer, 2017).44.Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    45.Levitus, S. & Mishonov, A. World Ocean Atlas 2013 (Vers. 2). NOAA Atlas NESDIS 73. National Oceanographic Data Center, Ocean Climate Laboratory United States, National Environmental Satellite Data Information Service (2013).46.Mienis, F. et al. Hydrodynamic controls on cold-water coral growth and carbonate-mound development at the SW and SE Rockall Trough Margin, NE Atlantic Ocean. Deep-Sea Res. Pt. I(54), 1655–1674 (2007).Article 

    Google Scholar 
    47.Sanfilippo, R. et al. Serpula aggregates and their role in deep-sea coral communities in the southern Adriatic Sea. Facies 59. https://doi.org/10.1007/s10347-012-0356-7 (2013).48.Hoey, J. A. & Pinsky, M. L. Genomic signatures of environmental selection despite near-panmixia in summer flounder. Evolut. Appl. 11, 1732–1747. https://doi.org/10.1111/eva.12676 (2018).CAS 
    Article 

    Google Scholar 
    49.Boavida, J., Becheler, R., Addamo, A. M., Sylvestre, F. & Arnaud-Haond, S. in Mediterranean Cold-Water Corals: Past, Present and Future, Springer Series: Coral Reefs of the World (eds. Covadonga Orejas Saco del Valle & C. Jiménez) (Springer, 2019).50.Sanford, E. & Kelly, M. W. Local adaptation in marine invertebrates. Ann. Rev. Mar. Sci. 3, 509–535. https://doi.org/10.1146/annurev-marine-120709-142756 (2011).Article 
    PubMed 

    Google Scholar 
    51.Frank, N. et al. Northeastern Atlantic cold-water coral reefs and climate. Geology 39, 743–746. https://doi.org/10.1130/g31825.1 (2011).ADS 
    Article 

    Google Scholar 
    52.Hebbeln, D. et al. ANNA cold-water coral ecosystems off Angola and Namibia. Cruise No. M122, December 30, 2015–January 31, 2016, Walvis Bay (Namibia) – Walvis Bay (Namibia). METEOR-Berichte, M122. DFG-Senatskommission Ozeanogr. 74. https://doi.org/10.2312/cr_m122 (2017).53.Vad, J., Orejas, C., Moreno-Navas, J., Findlay, H. S. & Roberts, J. M. Assessing the living and dead proportions of cold-water coral colonies: Implications for deep-water marine protected area monitoring in a changing ocean. PeerJ 5, e3705. https://doi.org/10.7717/peerj.3705 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    Computational sustainability meets materials science

    Computational sustainability research has been supported by an Expedition in Computing from the US National Science Foundation (NSF; CCF-1522054). eBird has been supported by the Leon Levy Foundation, the Wolf Creek Foundation, and NSF (DBI-1939187). Materials science research has also been supported by the AFOSR Multidisciplinary University Research Initiative (MURI) Program FA9550-18-1-0136, US DOE Award No.DE-SC0020383, and an award from the Toyota Research Institute. More

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    Cuticular hydrocarbons are associated with mating success and insecticide resistance in malaria vectors

    1.Tripet, F., Toure, Y. T., Dolo, G. & Lanzaro, G. C. Frequency of multiple inseminations in field-collected Anopheles gambiae females revealed by DNA analysis of transferred sperm. Am. J. Tropical Med. Hyg. 68, 1–5 (2003).Article 

    Google Scholar 
    2.Beehler, B. M. & Foster, M. S. Hotshots, hotspots, and female preference in the organization of lek mating systems. Am. Nat. 131, 203–219 (1988).Article 

    Google Scholar 
    3.Cator, L. J., Wyer, C. A. S. & Harrington, L. C. Mosquito sexual selection and reproductive control programs. Trends Parasitol. 37, 330–339 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Charlwood, J. D. & Jones, M. D. R. Mating behaviour in the mosquito, Anopheles gambiae s.1.save. Physiol. Entomol. 4, 111–120 (1979).Article 

    Google Scholar 
    5.Charlwood, J. D. et al. The swarming and mating behaviour of Anopheles gambiae s.s. (Diptera: Culicidae) from São Tomé Island. J. Vector Ecol. 27, 178–183 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Mozūraitis, R. et al. Male swarming aggregation pheromones increase female attraction and mating success among multiple African malaria vector mosquito species. Nat. Ecol. Evol. 1395–1401 (2020).7.Wang, G. et al. Clock genes and environmental cues coordinate Anopheles pheromone synthesis, swarming, and mating. Science 371, 411–415 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    8.Cator, L. J., Ng’Habi, K. R., Hoy, R. R. & Harrington, L. C. Sizing up a mate: variation in production and response to acoustic signals in Anopheles gambiae. Behav. Ecol. 21, 1033–1039 (2010).Article 

    Google Scholar 
    9.Pennetier, C., Warren, B., Dabiré, K. R., Russell, I. J. & Gibson, G. “Singing on the wing” as a mechanism for species recognition in the malarial mosquito Anopheles gambiae. Curr. Biol. 20, 131–136 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Simões, P. M., Gibson, G. & Russell, I. J. Pre-copula acoustic behaviour of males in the malarial mosquitoes Anopheles coluzzii and Anopheles gambiae s.s. does not contribute to reproductive isolation. J. Exp. Biol. 220, 379–385 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Maïga, H., Dabiré, R. K., Lehmann, T., Tripet, F. & Diabaté, A. Variation in energy reserves and role of body size in the mating system of Anopheles gambiae. J. Vector Ecol. 37, 289–297 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Sawadogo, S. P. et al. Effects of age and size on Anopheles gambiae s.s. male mosquito mating success. J. Med. Entomol. 50, 285–293 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Ng’habi, K. R. et al. Sexual selection in mosquito swarms: may the best man lose? Anim. Behav. 76, 105–112 (2008).Article 

    Google Scholar 
    14.Howell, P. I. & Knols, B. G. J. Male mating biology. Malar. J. 8, S8-S8, https://doi.org/10.1186/1475-2875-8-S2-S8 (2009).CAS 
    Article 

    Google Scholar 
    15.Aldersley, A. & Cator, L. J. Female resistance and harmonic convergence influence male mating success in Aedes aegypti. Sci. Rep. 9, 2145 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Pantoja-Sánchez, H., Gomez, S., Velez, V., Avila, F. W. & Alfonso-Parra, C. Precopulatory acoustic interactions of the New World malaria vector Anopheles albimanus (Diptera: Culicidae). Parasites Vectors 12, 386–386 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Ferveur, J.-F. & Cobb, M. Insect Hydrocarbons: Biology, Biochemistry, and Chemical Ecology. Cambridge University Press 325–343 (2010).18.Theresa, L. S. Roles of hydrocarbons in the recognition systems of insects. Am. Zool. 38, 394–405 (1998).Article 

    Google Scholar 
    19.Chung, H. et al. A single gene affects both ecological divergence and mate choice in Drosophila. Science 343, 1148–1151 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Grigoraki, L., Grau-Bové, X., Carrington Yates, H., Lycett, G. J. & Ranson, H. Isolation and transcriptomic analysis of Anopheles gambiae oenocytes enables the delineation of hydrocarbon biosynthesis. eLife 9, e58019 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Howard, R. W. & Blomquist, G. J. Ecological, behavioral, and biochemical aspects of insect hydrocarbons. Annu. Rev. Entomol. 50, 371–393 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Ingleby, F. C. Insect cuticular hydrocarbons as dynamic traits in sexual communication. Insects 6, 732–742 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Lang, J. T. & Foster, W. A. Is there a female sex pheromone in the mosquito Culiseta inornata? Environ. Entomol. 5, 1109–1115 (1976).Article 

    Google Scholar 
    24.Nijout, H. F. C. J. & George, B. Reproductive isolation in Stepgomyia mosquitoes. III Evidence for a sexual pheromone. Entomol. Exp. Appl. 14, 399–412 (1971).Article 

    Google Scholar 
    25.Lang, J. T. Contact sex pheromone in the mosquito Culiseta inornata (Diptera: Culicidae). J. Med. Entomol. 14, 448–454 (1977).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Polerstock, A. R., Eigenbrode, S. D. & Klowden, M. J. Mating alters the cuticular hydrocarbons of female Anopheles gambiae sensu stricto and aedes Aegypti (Diptera: Culicidae). J. Med. Entomol. 39, 545–552 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Balabanidou, V. et al. Cytochrome P450 associated with insecticide resistance catalyzes cuticular hydrocarbon production in Anopheles gambiae. Proc. Natl Acad. Sci. USA 113, 9268–9273 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Balabanidou, V. et al. Mosquitoes cloak their legs to resist insecticides. Proc. Biol. Sci. 286, 20191091 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Yahouedo, G. A. et al. Contributions of cuticle permeability and enzyme detoxification to pyrethroid resistance in the major malaria vector Anopheles gambiae. Sci. Rep. 7, 11091 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    30.Baeshen, R. et al. Differential effects of inbreeding and selection on male reproductive phenotype associated with the colonization and laboratory maintenance of Anopheles gambiae. Malar. J. 13, 19 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Toe, K. H. et al. Increased pyrethroid resistance in malaria vectors and decreased bed net effectiveness, Burkina Faso. Emerg. Infect. Dis. 20, 1691–1696 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.World Health Organization. Test Procedures for Insecticide Resistance Monitoring in Malaria Vector Mosquitoes. Geneva, Switzerland: World Health Organization (2013).33.Toe, K. H., N’Fale, S., Dabire, R. K., Ranson, H. & Jones, C. M. The recent escalation in strength of pyrethroid resistance in Anopheles coluzzi in West Africa is linked to increased expression of multiple gene families. BMC Genomics 16, 146 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    34.Kwiatkowska, R. M. et al. Dissecting the mechanisms responsible for the multiple insecticide resistance phenotype in Anopheles gambiae s.s., M form, from Vallee du Kou, Burkina Faso. Gene 519, 98–106 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Ingham, V. A. et al. Dissecting the organ specificity of insecticide resistance candidate genes in Anopheles gambiae: known and novel candidate genes. BMC Genomics 15, 1018 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    36.Blows, M. W. Interaction between natural and sexual selection during the evolution of mate recognition. Proc. Biol. Sci. 269, 1113–1118 (2002).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Lane, S. M., Dickinson, A. W., Tregenza, T. & House, C. M. Sexual selection on male cuticular hydrocarbons via male-male competition and female choice. J. Evol. Biol. 29, 1346–1355 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Steiger, S. et al. Sexual selection on cuticular hydrocarbons of male sagebrush crickets in the wild. Proc. Biol. Sci. 280, 20132353–20132353 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    39.Chung, H. & Carroll, S. B. Wax, sex and the origin of species: dual roles of insect cuticular hydrocarbons in adaptation and mating. Bioessays 37, 822–830, https://doi.org/10.1002/bies.201500014 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Sawadogo, S. P. et al. Differences in timing of mating swarms in sympatric populations of Anopheles coluzzii and Anopheles gambiae s.s. (formerly An. gambiae M and S molecular forms) in Burkina Faso, West Africa. Parasit. Vectors 6, 275 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Arcaz, A. C. et al. Desiccation tolerance in Anopheles coluzzii: the effects of spiracle size and cuticular hydrocarbons. J. Exp. Biol. 219, 1675–1688 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    42.Hidalgo, K. et al. Distinct physiological, biochemical and morphometric adjustments in the malaria vectors Anopheles gambiae and A. coluzzii as means to survive dry season conditions in Burkina Faso. J. Exp. Biol. 70, 102–116 (2018).43.Wagoner, K. M. et al. Identification of morphological and chemical markers of dry- and wet-season conditions in female Anopheles gambiae mosquitoes. Parasit. Vectors 7, 294 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.Wicker, C. & Jallon, J. M. Influence of ovary and ecdysteroids on pheromone biosynthesis in Drosophila melanogaster (Diptera: Drosophilidae). EJE 92, 197–202 (1995).CAS 

    Google Scholar 
    45.Andersson, M. Sexual Selection. Princeton University Press (1994).46.Fisher, R. The Genetical Theory of Natural Selection. The Clarendon Press, Oxford (1930).47.Weatherhead, P. J. & Robertson, R. J. Offspring quality and the polygyny threshold: “The Sexy Son Hypothesis”. Am. Nat. 113, 201–208 (1979).Article 

    Google Scholar 
    48.Ryan, M. J. Sexual selection, receiver biases, and the evolution of sex differences. Science 281, 1999–2003 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Rundle, H. D., Chenoweth, S. F. & Blows, M. W. The roles of natural and sexual selection during adaptation to a novel environment. Evolution 60, 2218–2225 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Thailayil, J., Magnusson, K., Godfray, H. C. J., Crisanti, A. & Catteruccia, F. Spermless males elicit large-scale female responses to mating in the malaria mosquito Anopheles gambiae. Proc. Natl Acad. Sci. USA 108, 13677–13681, https://doi.org/10.1073/pnas.1104738108 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Charlwood, J. D. Studies on the bionomics of male Anopheles gambiae Giles and male Anopheles funestus Giles from southern Mozambique. J. Vector Ecol. 36, 382–394, https://doi.org/10.1111/j.1948-7134.2011.00179.x (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Glunt, K. D., Thomas, M. B. & Read, A. F. The effects of age, exposure history and malaria infection on the susceptibility of Anopheles mosquitoes to low concentrations of pyrethroid. PLoS ONE 6, e24968–e24968 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Santolamazza, F. et al. Insertion polymorphisms of SINE200 retrotransposons within speciation islands of Anopheles gambiae molecular forms. Malar. J. 7, 163, https://doi.org/10.1186/1475-2875-7-163 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Diabaté, A. et al. Spatial distribution and male mating success of Anopheles gambiae swarms. BMC Evol. Biol. 11, 184–184, https://doi.org/10.1186/1471-2148-11-184 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Niang, A. et al. Does extreme asymmetric dominance promote hybridization between Anopheles coluzzii and Anopheles gambiae s.s. in seasonal malaria mosquito communities of West Africa? Parasit. Vectors 8, 586–586, https://doi.org/10.1186/s13071-015-1190-x (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Caputo, B. et al. Identification and composition of cuticular hydrocarbons of the major Afrotropical malaria vector Anopheles gambiae s.s. (Diptera: Culicidae): analysis of sexual dimorphism and age-related changes. J. Mass Spectrom. 40, 1595–1604, https://doi.org/10.1002/jms.961 (2005).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
    Article 

    Google Scholar 
    58.Charlwood, J. Biological variation in Anopheles darlingi root. Mem. Inst. Oswaldo Cruz. 91, 391–398 (1996).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

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    Biodiversity needs every tool in the box: use OECMs

    COMMENT
    26 July 2021

    Biodiversity needs every tool in the box: use OECMs

    To conserve global biodiversity, countries must forge equitable alliances that support sustainability in traditional pastoral lands, fisheries-management areas, Indigenous territories and more.

    Georgina G. Gurney

    0
    ,

    Emily S. Darling

    1
    ,

    Gabby N. Ahmadia

    2
    ,

    Vera N. Agostini

    3
    ,

    Natalie C. Ban

    4
    ,

    Jessica Blythe

    5
    ,

    Joachim Claudet

    6
    ,

    Graham Epstein

    7
    ,

    Estradivari

    8
    ,

    Amber Himes-Cornell

    9
    ,

    Harry D. Jonas

    10
    ,

    Derek Armitage

    11
    ,

    Stuart J. Campbell

    12
    ,

    Courtney Cox

    13
    ,

    Whitney. R. Friedman

    14
    ,

    David Gill

    15
    ,

    Peni Lestari

    16
    ,

    Sangeeta Mangubhai

    17
    ,

    Elizabeth McLeod

    18
    ,

    Nyawira A. Muthiga

    19
    ,

    Josheena Naggea

    20
    ,

    Ravaka Ranaivoson

    21
    ,

    Amelia Wenger

    22
    ,

    Irfan Yulianto

    23
    &

    Stacy D. Jupiter

    24

    Georgina G. Gurney

    Georgina G. Gurney is a senior research fellow in environmental social science at the Australian Research Council Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, Australia.

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    Emily S. Darling

    Emily S. Darling is director, Coral Reef Conservation, Wildlife Conservation Society.

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    Gabby N. Ahmadia

    Gabby N. Ahmadia is director, Marine Conservation Science, Ocean Conservation, World Wildlife Fund.

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    Vera N. Agostini

    Vera N. Agostini is deputy director, Fisheries and Aquaculture Division, Food and Agriculture Organization of the United Nations.

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    Natalie C. Ban

    Natalie C. Ban is associate professor in environmental studies at the University of Victoria, Canada.

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    Jessica Blythe

    Jessica Blythe is assistant professor in environmental sustainability at Brock University, St Catharines, Canada.

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    Joachim Claudet

    Joachim Claudet is a senior researcher at the National Center for Scientific Research, CRIOBE, France.

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    Graham Epstein

    Graham Epstein is a postdoctoral research associate at the School of Politics, Security and International Affairs at the University of Central Florida, Orlando, USA.

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    Estradivari

    Estradivari is a researcher at the Leibniz Center for Tropical Marine Research (ZMT), Germany, and a conservation research manager, World Wildlife Fund Indonesia.

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    Amber Himes-Cornell

    Amber Himes-Cornell is a fisheries officer, Fisheries and Aquaculture Division, Food and Agricultural Organization of the United Nations.

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    Harry D. Jonas

    Harry D. Jonas is an international lawyer at Future Law, Kota Kinabalu, Malaysia, and co-chair of the IUCN WCPA Specialist Group on Other Effective Area-based Conservation Measures.

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    Derek Armitage

    Derek Armitage is a professor in the School of Environment, Resources and Sustainability, University of Waterloo, Canada.

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    Stuart J. Campbell

    Stuart J. Campbell is senior director, RARE Indonesia.

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    Courtney Cox

    Courtney Cox is senior director, Rare, Washington DC, USA.

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    Whitney. R. Friedman

    Whitney R. Friedman is a postdoctoral fellow at the University of California, Santa Barbara, USA.

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    David Gill

    David Gill is an assistant professor of marine science and conservation at Duke University, Durham, North Carolina, USA.

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    Peni Lestari

    Peni Lestari is a socioeconomic marine specialist, Wildlife Conservation Society.

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    Sangeeta Mangubhai

    Sangeeta Mangubhai is director, Fiji Program, Wildlife Conservation Society.

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    Elizabeth McLeod

    Elizabeth McLeod is global reef lead, The Nature Conservancy, Arlington, Virginia, USA.

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    Nyawira A. Muthiga

    Nyawira A. Muthiga is director, Marine Conservation Program, Kenya Program, Wildlife Conservation Society.

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    Josheena Naggea

    Josheena Naggea is a PhD candidate at Stanford University, California, USA, studying conservation in her home country of Mauritius.

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    Ravaka Ranaivoson

    Ravaka Ranaivoson is marine director, Madagascar Program, Wildlife Conservation Society.

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    Amelia Wenger

    Amelia Wenger is a research fellow in the School of Earth and environmental sciences at the University of Queensland, Brisbane, Australia, and a conservation scientist at the Wildlife Conservation Society.

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    Irfan Yulianto

    Irfan Yulianto is a researcher and lecturer at Institut Pertanian Bogor University, Indonesia, and a Senior Manager, Wildlife Conservation Society.

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    Stacy D. Jupiter

    Stacy D. Jupiter is Melanesia regional director at the Wildlife Conservation Society.

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    Customary fishing-rights holders from Totoya Island, Fiji, marking a sacred reef area as a no-fishing zone.Credit: Keith Ellenbogen

    Global support is growing for the 30 × 30 movement — a goal to conserve 30% of the planet by 2030. In May, the G7 group of wealthy nations endorsed the commitment to this target that had been made by more than 50 countries in January. It is likely to be the headline goal when parties to the Convention on Biological Diversity (CBD) meet to discuss the latest global conservation agreement in May 2022 in Kunming, China.So where do the sacred forests of Estonia or shipwrecks in North America’s Great Lakes come in? What do these share with managed fishing grounds in Fiji and bighorn-sheep hunting areas in Mexico? All have the potential to be recognized using a conservation policy tool called other effective area-based conservation measures, or OECMs. Together with protected areas — from Malaysia’s Taman Negara National Park to the Cerbère-Banyuls Marine Reserve in southern France — OECMs could help to achieve the 30% target.Devised in 2010 and defined in 2018, the OECM tool is little known outside specialist circles. Less than 1% of the world’s land and freshwater environments and less than 0.1% of marine areas are currently covered under this designation. Meanwhile, biodiversity is in free fall and protected areas alone can’t stem the loss. Both designations are among the international policy instruments being negotiated ahead of the CBD conference.We call on the CBD parties and the conservation community of policymakers, scientists, practitioners and donors to study and use OECMs more, alongside protected areas. This policy tool can advance equitable and effective conservation if CBD parties stay true to the convention’s intent to sustain biodiversity rather than ‘achieve’ area-based targets. But more groundwork must be laid to realize its potential.Improvements are needed in research, policy and practice. Local managers and CBD parties need better ways to assess whether potential OECMs contribute to sustaining biodiversity, so that areas are properly designated. The conservation community needs to develop processes to ensure that OECM recognition strengthens, rather than displaces, existing local governance. And researchers need to articulate the value of OECMs to encourage policymakers to use them.Bigger toolkit Protected areas have expanded rapidly in the past 10 years, and now cover 15.7% of the world’s land and fresh water, and 7.7% of the marine realm. Defined by the CBD as areas designated or regulated and managed for biodiversity conservation, they are an essential conservation approach. But some have failed to be equitable or effective: aligning biodiversity goals with local values, needs and governance can be difficult in some contexts1,2. This conflict can lead to inequities, non-compliance and poor biodiversity outcomes.
    Indigenous rights vital to survival
    OECMs can have an important and complementary role3. The tool recognizes managed areas that sustain biodiversity, irrespective of their objective. OECM recognition can support Indigenous and local communities in managing their lands and seas — be it for hunting, fishing or other cultural practices — while conserving nature. It opens up new conservation opportunities in landscapes where there is relatively light human usage, such as pastoralism with a low density of livestock. These regions make up nearly 56% of the world’s lands, and contain more Key Biodiversity Areas — sites of global important to biodiversity — than do remaining large wild areas4. So, management approaches that accommodate the ways people use landscapes and seascapes are crucial.Some managed areas do not safeguard biodiversity5. But there is a wealth of evidence suggesting that many do. For example, a study of the Peruvian Amazon found that Indigenous peoples’ territories were, on average, more effective than state-governed protected areas at preventing deforestation6. A review of 61 areas managed under territorial-use rights in fisheries in Chile found positive effects on biodiversity; some had levels of fish biomass and biodiversity that were comparable to those in a protected area that restricts all fishing7. And abandonment of agricultural management systems involving low-intensity farming methods in Europe — such as traditional haymaking in Romania — has been linked repeatedly to biodiversity loss8.Perhaps many of these could be recognized as OECMs (see ‘Conservation potential’). Doing so depends on the consent of the relevant governing bodies, and whether the managed area meets the CBD’s definition and criteria for OECMs, including demonstrated or expected biodiversity outcomes.

    EquityOECMs can help to ensure that international conservation targets are legitimate to the many and diverse actors required to turn the tide on biodiversity loss.Too often, the costs of conservation are felt locally while many of the benefits are shared globally — from carbon sequestration to preserving genetic resources. For instance, rainforest conservation, including a protected area, in the Ankeniheny-Zahamena Corridor in Madagascar meant that local farmers of vanilla, cloves and rice bore opportunity costs representing 27–84% of their average annual household income. The protection scheme is intended to cut 10 million tonnes of carbon dioxide emissions over 10 years9.Such inequities can occur when protected areas do not prioritize local values and needs. Although protected areas can have multiple objectives, the widely followed guidance from the International Union for Conservation of Nature (IUCN) advises that nature conservation should retain priority over all other objectives. This can alienate people who manage areas for other reasons. Even in the instance of Indigenous Protected Areas in Australia, which have resulted in an array of social and biodiversity benefits, the IUCN definition can undermine Indigenous Australians’ conceptualization of humans as part of nature, which underpins their governance systems2. This stands in contrast to the Western world view of humans as distinct from nature — a concept that is embedded in the IUCN definition and conservation more generally2,3.
    A spatial overview of the global importance of Indigenous lands for conservation
    However, OECMs need not have conservation as an objective. This means that they can be used to recognize the contributions of a myriad of actors who manage areas that sustain nature, regardless of why they do so. Indigenous peoples, for instance, manage 37% of the world’s natural lands10 for many reasons, such as maintaining rights, harvesting and cultural identity2,10,11. Recognition of Indigenous territories as OECMs could help to overcome current challenges of insecure rights, insufficient funding and exclusion of these communities from decision-making12. For example, Indonesia has initiated revisions to its conservation laws to accommodate coastal OECMs, which could provide opportunities for Indigenous and local communities to gain legal recognition of their rights to use and manage fisheries.OECMs can thus ensure a more equitable approach to conservation decision-making. They enable the participation of those who govern areas that sustain biodiversity but who are currently not involved in decision-making. For example, fisheries-management organizations have rebuilt some fish stocks, contributing to biodiversity and wider ecosystem health, yet the fisheries and conservation sectors are often divided13. OECMs can foster cooperation between sectors, and encourage the participation of fisheries-management organizations in conservation decision-making.EffectivenessCollectively, alongside protected areas, OECMs can increase the effectiveness of the global conservation system in four key ways.First, they support management that is tailored to its context14, and aligned with local values, governance and traditional knowledge systems. This fosters the local leadership, support and compliance that are key to biodiversity benefits14. For example, in Mo’orea, French Polynesia, protected areas that restricted all fishing did not meet fishers’ needs, leading to non-compliance and relatively little change in the density and biomass of coral-reef fish15. Conversely, a management area in Labrador, Canada, implemented at the behest of crab fishers, maintained the fishery and increased the biomass of fish species such as Atlantic cod (Gadus morhua) and other, non-target species16. This area seems likely to meet the OECM criteria.

    Estonia’s sacred groves are protected for their spiritual significance.Credit: Toomas Tuul/FOCUS/Universal Images Group via Getty

    Second, OECMs, together with protected areas, can help to ensure a well-connected conservation network in which all elements of biodiversity are represented and in which ecological processes, such as species movements, are sustained. For instance, Kenya’s wildlife conservancies provide geographical bridges between protected areas for the movement of wildlife such as zebras, and have potential to be recognized as OECMs.Third, OECMs can increase the diversity of tools in the global conservation system. This bolsters the system’s resilience to social and biophysical shifts, including climate change14. Redundancy in governance arrangements can help to mitigate risks associated with the current reliance on government-led protected areas, which are vulnerable to shifts in national priorities. For example, in 2017, the Bears Ears National Monument, a protected area in Utah, was downsized by 85% to make way for oil and gas exploration under a former US presidential administration.Fourth, OECMs help to bring conservation outcomes into focus. A key criterion for official designation is demonstrated or expected biodiversity outcomes, such as the restoration of a crucial habitat. This is not the case for protected areas, where a focus on coverage has, in some cases, led to expansion with scant biodiversity gains4.Five steps Key concerns remain about the misuse of OECM recognition. CBD parties might use it to meet commitments without actually tackling biodiversity loss. For example, in 2017, Canada increased the marine area it planned to report almost sixfold, by reclassifying 51 fishery closures as OECMs17. This decision was criticized on the grounds of insufficient evidence that these areas sustain biodiversity. Another concern is that, despite the focus on equity, any attempts to influence local governance could be perceived as a ‘land grab’ or ‘sea grab’ by external actors such as national governments, foreigners or international organizations. For example, the establishment of some privately owned protected areas in southern Chile has been suggested to have involved coercion and intimidation of smallholder farmers.
    Area-based conservation in the twenty-first century
    The conservation community needs to take the following five steps to overcome these key challenges to using the OECM policy tool.Show that they work. The 2019 IUCN Guidelines for Recognizing and Reporting OECMs provide clear criteria for identifying managed areas that are suitable for a full assessment against the CBD’s definition. However, research is needed on how to meet the crucial criteria of demonstrated or expected in situ conservation of biodiversity. This is challenging and resource-intensive, especially because of the variety of actors involved. Ideas based in Western science might not align with the knowledge systems of all involved.Guidelines should build on existing approaches for evaluation, such as the IUCN Green List for Protected and Conserved Areas and the Indicators of Resilience in Socio-ecological Production Landscapes (SEPLs). They should include recent advances focused on outcomes18 and should be tailored to different types of managed area. To ensure that these are salient, credible and legitimate to those governing OECMs, they should be co-produced by groups such as rights holders, civil-society organizations, government and industry, as well as by academics from various disciplines. This transdisciplinary approach is growing rapidly, with examples ranging from management at the national level (such as New Zealand’s Sustainable Seas National Science Challenge) to the monitoring of coral reefs as social-ecological systems19.

    Pastoral lands in Africa are often governed to maintain sustainable grazing.Credit: Steve Pastor

    Strengthen existing local governance. Many rights holders have raised concerns that formal recognition of their managed areas for conservation might infringe their rights. For example, few communities in Fiji have had their fisheries-management areas recognized under national conservation laws, because that currently requires the communities to waive their customary rights20.Engaging with global conservation processes might also erode self-determination through the imposition of external world views2,3,12. Although OECMs open the door to recognizing diverse relations between humans and nature, it is crucial that the need for demonstrated or expected biodiversity outcomes does not diminish other priorities and values.OECM recognition must strengthen existing local governance, rather than displace or substantially alter it. This will require guidelines to be informed by principles of procedural equity and tailored to different types of managed area. Their development should draw on existing approaches such as the Australian Indigenous-led Healthy Country Planning and Our Knowledge, Our Way guidelines, which have underpinned engagement with the national carbon sequestration scheme11.Secure funding. Funding for recognizing and reporting OECMs should be made available to ensure costs are not a barrier or burden for under-resourced groups. A prominent role for OECMs in the next CBD agreement will help — this policy guides conservation investments from nations and donors.
    Sixty years of tracking conservation progress using the World Database on Protected Areas
    Importantly, the diversity of managed areas that OECMs encompass can provide funding opportunities beyond conventional conservation funders, whose resources for protected-area funding are already overstretched. Conservation practitioners should engage private sectors that manage areas that could be recognized as OECMs, and access funding earmarked for other priorities such as health and development. For example, the Watershed Interventions for Systems Health project in Fiji, which aims to reduce waterborne diseases using nature-based solutions, is supported by both conservation and public-health funding.Conservation donors and practitioners should co-design new funding strategies for OECMs with those governing these areas. This will help to ensure that local priorities are supported. For example, Coast Funds, a unique conservation trust fund, was developed by First Nations people in collaboration with conservation practitioners and the forestry industry to support stewardship of the Great Bear Rainforest and Haida Gwaii regions of British Columbia, Canada.Agree on metrics. The record of progress towards the CBD’s area-based target, the World Database on Protected Areas, assumes that all reported protected areas have biodiversity conservation as a main objective. But some CBD parties report areas that have other primary objectives, such as sustainable harvesting20. This leads to inaccurate accounting at the global level, and to misunderstanding of management actually occurring on the ground. Canada, among others, is developing legislation that demarcates protected areas and OECMs. But it is not clear whether all CBD parties will do the same.Policymakers need to agree on targets that are based on outcomes — not just coverage — for both OECMs and protected areas. These might include, for example, changes in the populations of multiple species relative to a reference point. In constructing these targets, the conservation community should be guided by the development and health sectors, which have long used outcome targets. For example, the United Nations Sustainable Development Goal 1.2 aims to reduce at least by half the proportion of people living in multidimensional, regionally-defined poverty by 2030. A common currency of outcomes could alleviate concerns that there is an uneven burden of proof for the OECM and protected-area tools. It could also prevent the misuse of either to meet targets based on area without actually sustaining biodiversity.Include OECMs in other environmental agreements. Addressing the interrelated global challenges of biodiversity loss, climate change and sustainability requires the coordination of policy across sectors. Right now, OECMs appear only in CBD policy. But they could contribute to the mandates of other intergovernmental initiatives. Policymakers should include OECMs alongside protected areas in international agreements such as the Sustainable Development Goals, new global climate agreements being negotiated under the UN convention on climate, and the emerging UN treaty on marine biodiversity in areas beyond national jurisdiction.New targets negotiated at the upcoming CBD meeting will set the global conservation agenda over the next decade. If the steps we outline here are implemented, OECMs could be central to the transformations needed for a sustainable future for the planet.

    Nature 595, 646-649 (2021)
    doi: https://doi.org/10.1038/d41586-021-02041-4

    References1.Oldekop, J. A., Holmes, G., Harris, W. E. & Evans, K. L. Conserv. Biol. 30, 133–141 (2016).PubMed 
    Article 

    Google Scholar 
    2.Lee, E. Antipode 48, 355–374 (2016).Article 

    Google Scholar 
    3.Jonas, H. D., Barbuto, V., Jonas, H. C., Kothari, A. & Nelson, F. PARKS 20, 111–128 (2014).Article 

    Google Scholar 
    4.Ellis, E. C. One Earth 1, 163–167 (2019).Article 

    Google Scholar 
    5.Donald, P. F. et al. Conserv. Lett. 12, e12659 (2019).Article 

    Google Scholar 
    6.Schleicher, J., Peres, C. A., Amano, T., Llactayo, W. & Leader-Williams, N. Sci. Rep. 7, 11318 (2017).PubMed 
    Article 

    Google Scholar 
    7.Gelcich, S., Martínez-Harms, M. J., Tapia-Lewin, S., Vasquez-Lavin, F. & Ruano-Chamorro, C. Conserv. Lett. 12, e12637 (2019).Article 

    Google Scholar 
    8.Lomba, A. et al. Front. Ecol. Environ. 18, 36–42 (2020).Article 

    Google Scholar 
    9.Poudyal, M. et al. PeerJ 6,e5106 (2018).PubMed 
    Article 

    Google Scholar 
    10.Garnett, S. T. et al. Nature Sustain. 1, 369–374 (2018).Article 

    Google Scholar 
    11.Ansell, J. et al. Int. J. Wildland Fire 29, 371–385 (2019).Article 

    Google Scholar 
    12.Corson, C. et al. Conserv. Soc. 12, 190–202 (2014).Article 

    Google Scholar 
    13.Hilborn, R. Nature 535, 224–226 (2016).PubMed 
    Article 

    Google Scholar 
    14.Carlisle, K. & Gruby, R. L. Policy Stud. J. 47, 927–952 (2019).Article 

    Google Scholar 
    15.Thiault, L. et al. Ecosphere 10, e02576 (2019).Article 

    Google Scholar 
    16.Kincaid, K. & Rose, G. Can. J. Fish. Aqua. Sci. 74, 1490–1502 (2017).Article 

    Google Scholar 
    17.Lemieux, C. J. & Gray, P. A. J. Environ. Stud. Sci. 10, 483–491 (2020).Article 

    Google Scholar 
    18.Geldmann, J. et al. Conserv. Lett. https://doi.org/10.1111/conl.12792 (2021).Article 

    Google Scholar 
    19.Gurney, G. G. et al. Biol. Conserv. 240, 108298 (2019).Article 

    Google Scholar 
    20.Govan, H. & Jupiter, S. PARKS 19, 73–80 (2013).Article 

    Google Scholar 
    Download references

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    Niche partitioning among dead wood-dependent beetles

    1.Polechová, J. & Storch, D. Ecological niche. Encycl. Ecol. 2, 1088–1097 (2008).
    Google Scholar 
    2.Vannette, R. L. & Fukami, T. Historical contingency in species interactions: Towards niche-based predictions. Ecol. Lett. 17, 115–124 (2014).PubMed 
    Article 

    Google Scholar 
    3.Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton University Press, 2011).Book 

    Google Scholar 
    4.Clark, J. S. The coherence problem with the unified neutral theory of biodiversity. Trends Ecol. Evol. 27, 198–202 (2012).PubMed 
    Article 

    Google Scholar 
    5.McGill, B. J. A test of the unified neutral theory of biodiversity. Nature 422, 881–885 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Bocci, A. et al. Sympatric snow leopards and Tibetan wolves: Coexistence of large carnivores with human-driven potential competition. Eur. J. Wildl. Res. 63, 92 (2017).Article 

    Google Scholar 
    7.Dueser, R. D. & Shuggart, H. H. Niche pattern in a forest-floor small-mammal fauna. Ecology 60, 108–118 (1979).Article 

    Google Scholar 
    8.Cloyed, C. S. & Eason, P. K. Niche partitioning and the role of intraspecific niche variation in structuring a guild of generalist anurans. R. Soc. Open Sci. 4, 170060 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Armstrong, R. A. & McGehee, R. Coexistence of species competing for shared resources. Theor. Popul. Biol. 9, 317–328 (1976).MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    10.Paillet, Y. et al. The indicator side of tree microhabitats: A multi-taxon approach based on bats, birds and saproxylic beetles. J. Appl. Ecol. 55, 2147–2159 (2018).Article 

    Google Scholar 
    11.Kadowaki, K. Species coexistence patterns in a mycophagous insect community inhabiting the wood-decaying bracket fungus Cryptoporus volvatus (Polyporaceae: Basidiomycota). Eur. J. Entomol. 107, 89 (2010).Article 

    Google Scholar 
    12.Peter, A.-K. Survival in adults of the water frog Rana lessonae and its hybridogenetic associate Rana esculenta. Can. J. Zool. 79, 652–661 (2001).Article 

    Google Scholar 
    13.Borkowski, A. & Skrzecz, I. Ecological segregation of bark beetle (Coleoptera, Curculionidae, Scolytinae) infested Scots pine. Ecol. Res. 31, 135–144 (2016).Article 

    Google Scholar 
    14.Bobiec, A., Gutowski, J. M. & Laudenslayer, W. F. The Afterlife of a Tree (WWF Poland, 2005).
    Google Scholar 
    15.Alexander, K. N. Tree biology and saproxylic Coleoptera: issues of definitions and conservation language. Rev. Ecol. 10, 9–13 (2008).
    Google Scholar 
    16.Véle, A. & Horák, J. The importance of host characteristics and canopy openness for pest management in urban forests. Urban For. Urban Green. 36, 84–89 (2018).Article 

    Google Scholar 
    17.Přikryl, Z. B., Turčáni, M. & Horák, J. Sharing the same space: Foraging behaviour of saproxylic beetles in relation to dietary components of morphologically similar larvae. Ecol. Entomol. 37, 117–123 (2012).Article 

    Google Scholar 
    18.Brin, A. & Bouget, C. Biotic interactions between saproxylic insect species. In Saproxylic insects: Diversity, ecology and conservation (ed. Ulyshen, M. D.) 471–514 (Springer, 2018).Chapter 

    Google Scholar 
    19.Stokland, J. N., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood (Cambridge University Press, 2012).Book 

    Google Scholar 
    20.Radchuk, V., Turlure, C. & Schtickzelle, N. Each life stage matters: The importance of assessing the response to climate change over the complete life cycle in butterflies. J. Anim. Ecol. 82, 275–285 (2013).PubMed 
    Article 

    Google Scholar 
    21.Biedermann, P. H. & Taborsky, M. Larval helpers and age polyethism in ambrosia beetles. Proc. Natl. Acad. Sci. U.S.A. 108, 17064–17069 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Hanks, L. M. Influence of the larval host plant on reproductive strategies of cerambycid beetles. Annu. Rev. Entomol. 44, 483–505 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Horak, J. What is happening after an abiotic disturbance? Response of saproxylic beetles in the Primorsky Region woodlands (Far Eastern Russia). J. Insect Conserv. 19, 97–103 (2015).Article 

    Google Scholar 
    24.Hůrka, K. Beetles of the Czech and Slovak Republics (Kabourek, 2005).
    Google Scholar 
    25.Horák, J. & Chobot, K. Phenology and notes on the behaviour of Cucujus cinnaberinus: Points for understanding the conservation of the saproxylic beetle. North-West. J. Zool. 7, 352–355 (2011).
    Google Scholar 
    26.Finke, D. L. & Snyder, W. E. Niche partitioning increases resource exploitation by diverse communities. Science 321, 1488–1490 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Crowson, R. Observations on Dendrophagus crenatus (Paykull)(Cucujidae) and some comparisons with piestine Staphylinidae (Coleoptera). Entomol. Mon. Mag. 104, 161–169 (1969).
    Google Scholar 
    28.Tarno, H. et al. The behavioral role of males of platypus quercivorus murayama in their subsocial colonies. Agrivita 38, 47–54 (2016).
    Google Scholar 
    29.Della Rocca, F. & Milanesi, P. Combining climate, land use change and dispersal to predict the distribution of endangered species with limited vagility. J. Biogeogr. 47, 1427–1438 (2020).Article 

    Google Scholar 
    30.Buse, J. “Ghosts of the past”: flightless saproxylic weevils (Coleoptera: Curculionidae) are relict species in ancient woodlands. J. Insect Conserv. 16, 93–102 (2012).Article 

    Google Scholar 
    31.Røed, K. H. et al. Isolation and characterization of ten microsatellite loci for the wood-living and threatened beetle Cucujus cinnaberinus (Coleoptera: Cucujidae). Conserv. Genet. Resour. 6, 641–643 (2014).Article 

    Google Scholar 
    32.Konvicka, M., Hula, V. & Fric, Z. Habitat of pre-hibernating larvae of the endangered butterfly Euphydryas aurinia (Lepidoptera: Nymphalidae): What can be learned from vegetation composition and architecture?. Eur. J. Entomol. 100, 313–322 (2003).Article 

    Google Scholar 
    33.Bonacci, T. et al. Artificial feeding and laboratory rearing of endangered saproxylic beetles as a tool for insect conservation. J. Insect Sci. 20, 20 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Mazzei, A. et al. Rediscovering the ‘umbrella species’ candidate Cucujus cinnaberinus (Scopoli, 1763) in Southern Italy (Coleoptera Cucujidae), and notes on bionomy. Ital. J. Zool. 78, 264–270 (2011).Article 

    Google Scholar 
    35.Horák, J., Chumanová, E. & Chobot, K. Habitat preferences influencing populations, distribution and conservation of the endangered saproxylic beetle Cucujus cinnaberinus (Coleoptera: Cucujidae) at the landscape level. Eur. J. Entomol. 107, 81–88 (2010).Article 

    Google Scholar 
    36.Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Carmel, Y. et al. Using exclusion rate to unify niche and neutral perspectives on coexistence. Oikos 126, 1451–1458 (2017).Article 

    Google Scholar 
    38.Horák, J., Chumanová, E. & Hilszczański, J. Saproxylic beetle thrives on the openness in management: a case study on the ecological requirements of Cucujus cinnaberinus from Central Europe. Insect Conserv. Divers. 5, 403–413 (2012).Article 

    Google Scholar 
    39.Keddy, P. Competiton 2nd edn. (Springer, 2001).Book 

    Google Scholar 
    40.Bonacci, T. et al. Beetles “in red”: are the endangered flat bark beetles Cucujus cinnaberinus and C. haematodes chemically protected? (Coleoptera: Cucujidae). Eur. Zool. J. 85, 128–136 (2018).Article 
    CAS 

    Google Scholar 
    41.Chararas, C., Chipoulet, J. M. & Courtois, J. E. Purification partielle et caracterisation d’une beta-glucosidase des larves de Pyrochroa coccinea (Coleoptere, Pyrochroidae). C. R. Séances Soc. Biol. Fil. 1771, 22–27 (1983).
    Google Scholar 
    42.Dettner, K. Description of defensive glands from cardinal beetles (Coleoptera, Pyrochroidae)—their phylogenetic significance as compared with other heteromeran defensive glands. Entomol. Basil. 9, 204–215 (1984).
    Google Scholar 
    43.Nardi, G. & Bologna, M. Cantharidin attraction in Pyrochroa (Coleoptera: Pyrochroidae). Entomol. News 111, 74–75 (2000).
    Google Scholar 
    44.Hirzel, A. & Guisan, A. Which is the optimal sampling strategy for habitat suitability modelling. Ecol. Model. 157, 331–341 (2002).Article 

    Google Scholar 
    45.Jaworski, T. et al. Saproxylic moths reveal complex within-group and group-environment patterns. J. Insect Conserv. 20, 677–690 (2016).Article 

    Google Scholar 
    46.Gotelli, N. J., Hart, E. M. & Ellison, A. M. EcoSimR: Null Model Analysis for Ecologicaldata. R package version 0.1.0 (Zenodo, 2015).47.Heiberger, R. M. HH: Statistical Analysis and Data Display: Heiberger and Holland. https://CRAN.R-project.org/package=HH (2020).48.Walsh, C. & Mac Nally, R. M. Hier.Part: Hierarchical partitioning. https://cran.r-project.org/web/packages/hier.part/index.html (2020). More

  • in

    The isotopic niche of Atlantic, biting marine mammals and its relationship to skull morphology and body size

    1.Pauly, D., Trites, A. W., Capuli, E. & Christensen, V. Diet composition and trophic levels of marine mammals. ICES J. Mar. Sci. 55, 467–481 (1998).Article 

    Google Scholar 
    2.Wilson, D. E. & Mittermeier, R. A. Handbook of the mammals of the world. Sea mammals (Lynx Edicions 2014).3.Plagányi, E. E. & Butterworth, E. S. Competition with fisheries in Encyclopedia of Marine Mammals (eds W. F. Perrin, B. Würsing, & J. G. M. Thewsissen) 269–275 (Academic Press, 2009).4.Read, A. J. The looming crisis: interactions between marine mammals and fisheries. J. Mammal. 89, 541–548 (2008).Article 

    Google Scholar 
    5.Morissette, L., Christensen, V. & Pauly, D. Marine mammal impacts in exploited ecosystems: would large scale culling benefit fisheries? PLoS One 7, e43966 (2012).6.Gerber, L. R., Morissette, L., Kaschner, K. & Pauly, D. Should whales be culled to increase fishery yield?. Science 323, 880–881 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    7.DeMaster, D. P., Fowler, C. W., Perry, S. L. & Richlen, M. F. Predation and competition: the impact of fisheries on marine-mammals populations over the next one hundred years. J. Mammal. 82, 641–651 (2001).Article 

    Google Scholar 
    8.Smith, T. D. Interactions between marine mammals and fisheries: an unresolved problem for fisheries research in Whales, seals, fish and man (eds A.S. Blix, L. Walløe, & t Ø. Ultan) 527–536 (Elsevier Science, 1995).9.Hall, A. J., Watkins, J. & Hammond, P. S. Seasonal variation in the diet of harbour seals in the south-western North Sea. Mar. Ecol. Prog. Ser. 170, 269–281 (1998).ADS 
    Article 

    Google Scholar 
    10.Santos, M. B., Martin, V., Fernández, A. & Pierce, G. J. Insights into the diet of beaked whales from the atypical mass stranding in the Canary Islands in September 2002. J. Mar. Biol. Assoc. U. K. 87, 243–251 (2007).Article 

    Google Scholar 
    11.Gómez-Campos, E., Borrell, A., Cardona, L., Forcada, J. & Aguilar, A. Overfishing of small pelagic fishes increases trophic overlap between immature and mature striped dolphins in the Mediterranean sea. PLoS One 6, e24554 (2011).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    12.Adam, P. J. & Berta, A. Evolution of prey capture strategies and diet in the pinnipedimorpha (Mammalia, Carnivora). Oryctos 4, 83–107 (2002).
    Google Scholar 
    13.Kienle, S. S. & Berta, A. The better to eat you with: the comparative feeding morphology of phocid seals (Pinnipedia, Phocidae). J. Anat. 228, 396–413 (2016).PubMed 
    Article 

    Google Scholar 
    14.McCurry, M. R., Fitzgerald, E. M. G., Evans, A. R., Adams, J. W. & McHenry, C. R. Skull shape reflects prey size niche in toothed whales. Biol. J. Linn. Soc. 121, 936–946 (2017).Article 

    Google Scholar 
    15.McCurry, M. R. et al. The remarkable convergence of skull shape in crocodilians and toothed whales. Proc. R. Soc. B 284, 20162348 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Davis, R. W. Marine Mammals: adaptations for an aquatic life (Springer, 2019).Book 

    Google Scholar 
    17.Marshall, C. D. & Pyenson, N. D. Feeding in aquatic mammals: an evolutionary and functional approach in Feeding in vertebrates: evolution, morphology, behaviour, biomechanics. Fascinating Life Sciences (eds V. Bels & I. Whishaw) 743–785 (Springer, Cham, 2019).18.Werth, A. J. Mandibular and dental variation and the evolution of suction feeding in Odontoceti. J. Mammal. 87, 579–588 (2006).Article 

    Google Scholar 
    19.Kelley, N. P. & Motani, R. Trophic convergence drives morphological convergence in marine tetrapods. Biol. Lett. 11, 20140709 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Kienle, S. S., Law, C. J., Costa, D. P., Berta, A. & Mehta, R. S. Revisiting the behavioural framework of feeding in predatory aquatic mammals. Proc. R. Soc. B 284, 20171035 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Segura, A. M., Franco-Trecu, V., Franco-Fraguas, P. & Arim, M. Gape and energy limitation determining a humped relationship between trophic position and body size. Can. J. Fish. Aquat. Sci. 72, 198–205 (2015).CAS 
    Article 

    Google Scholar 
    22.Taylor, M. A. How tetrapods feed in water: a functional analysis by paradigm. Zool. J. Linn. Soc. 91, 171–195 (1987).Article 

    Google Scholar 
    23.Werth, A. Feeding in marine mammals in Feeding: form, function, and evolution in tetrapod vertebrates (ed K. Schwenk) 487–526 (Academic Press, 2010).24.Hocking, D. P., Salverson, M., Fitzgerald, E. M. G. & Evans, A. R. Australian fur seals (Arctocephalus pusillus doriferus) use raptorial biting and suction feeding when targeting prey in different foraging scenarios. PLoS One 9, e112521 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    25.Dalerum, F. & Angerbjörn, A. Resolving temporal variation in vertebrate diets using naturally occurring stable isotopes. Oecologia 144, 647–658 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Bearhop, S., Adams, C. E., Waldrons, S., Fuller, R. A. & Macleod, H. Determining trophic niche width: a novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).Article 

    Google Scholar 
    27.Layman, C. A., Arrington, D. A., Montanä, C. G. & Post, D. M. Can stable isotope ratios provide for community-wide measures of trophic structure?. Ecology 88, 42–48 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.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 
    29.Michener, R. H. & Lajtha, K. Stable isotopes in ecology and environmental science. Second edn, (Blackwell publishing, 2007).30.Post, D. M. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83, 703–718 (2002).Article 

    Google Scholar 
    31.Das, K. et al. Marine mammals from northeast Atlantic: relationship between their trophic status as determined by d13C and d15N measurements and their trace metal concentration. Mar. Environ. Res. 56, 349–365 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Das, K., Lepoint, G., Leroy, Y. & Bouquegneau, J. M. Marine mammals from the southern North Sea: feeding ecology data from d13C and d15N measurements. Mar. Ecol. Prog. Ser. 263, 287–298 (2003).ADS 
    Article 

    Google Scholar 
    33.Mèndez-Fernandez, P. et al. Foraging ecology of five toothed whale species in the Northwest Iberian Peninsula, inferred using carbon and nitrogen isotope ratios. J. Exp. Mar. Biol. Ecol. 413, 150–158 (2012).Article 
    CAS 

    Google Scholar 
    34.Pinela, A. M., Borrell, A., Cardona, L. & Aguilar, A. Stable isotope analysis reveals habitat partitioning among marine mammals off the NW African coast and unique trophic niches for two globally threatened species. Mar. Ecol. Prog. Ser. 416, 295–306 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Costa, A. F., Botta, S., Siciliano, S. & Giarrizzo, T. Resource partitioning among stranded aquatic mammals from Amazon and northeastern coast of Brazil revealed through carbon and nitrogen stable isotopes. Sci. Rep. 10, 12897 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Bisi, T. L. et al. Trophic relationships and habitat preferences of delphinids from the southeastern Brazilian coast determined by carbon and nitrogen stable isotope composition. PLoS One 8, e82205 (2013).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    37.Riccialdelli, L., Newsome, S. D., Fogel, M. L. & Goodall, R. N. Isotopic assessment of prey and habitat preferences of a cetacean community in the southwestern South Atlantic Ocean. Mar. Ecol. Prog. Ser. 418, 235–248 (2010).ADS 
    Article 

    Google Scholar 
    38.Saporiti, F. et al. Resource partitioning among air-breathing marine predators: are body size and mouth diameter the major determinants?. Mar. Ecol. 37, 957–969 (2016).ADS 
    Article 

    Google Scholar 
    39.Ford, J. K. B. Killer whale Orcinus orca in Encyclopedia of Marine Mammals (eds B. Würsig, J.G.M. Thewissen, & K.M. Kovacs) 531–537 (Academic Press, 2018).40.Durban, J. W. & Pitman, R. L. Antarctic killer whales make rapid, round-trip movements to subtropical waters: evidence for physiological maintenance migrations?. Biol. Lett. 8, 274–277 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Drago, M. et al. Mouth gape determines the response of marine top predators to long-term fishery-induced changes in food web structure. Sci. Rep. 8, 15759 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    42.Drago, M. et al. Isotopic niche partitioning between two apex predators over time. J. Anim. Ecol. 86, 766–780 (2017).PubMed 
    Article 

    Google Scholar 
    43.Bond, A. L. & Hobson, K. A. Reporting stable-isotope ratios in ecology: Recommended terminology, guidelines and best practices. Waterbirds 35, 324–331 (2012).Article 

    Google Scholar 
    44.Skrzypek, G. Normalization procedures and reference material selection in stable HCNOS isotope analyses: an overview. Anal. Bioanal. Chem. 405, 2815–2823 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572 (2010).CAS 

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

    Google Scholar 
    47.Verburg, P. The need to correct for the Suess effect in the application of δ13C in sediment of autotrophic Lake Tanganyika, as a productivity proxy in the Anthropocene. J. Paleolimnol. 37, 591–602 (2007).ADS 
    Article 

    Google Scholar 
    48.Gruber, N. et al. Spatiotemporal patterns of carbon-13 in the global surface oceans and the oceanic Suess effect. Global Biogeochem. Cycles 13, 307–335 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    49.Quay, P., Sonnerup, R., Westby, T., Stutsman, J. & McNichol, A. Changes in the 13C/12C of dissolved inorganic carbon in the ocean as a tracer of anthropogenic CO2 uptake. Global Biogeochem. Cycles 17, 1004 (2003).ADS 
    Article 
    CAS 

    Google Scholar 
    50.Borrell, A., Abad-Oliva, N., Gómez-Campos, E., Giménez, J. & Aguilar, A. Discrimination of stable isotopes in fin whale tissues and application to diet assessment in cetaceans. Rapid Commun. Mass Spectrom. 26, 1596–1602 (2012).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.McMahon, K. W., Hamady, L. L. & Thorrold, S. R. A review of ecogeochemistry approaches to estimating movements of marine animals. Limnol. Oceanogr. 58, 697–714 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    52.R Core Team. R: A language and environment for statistical computing, http://www.R-project.org. (2018).53.Hobson, K. A. & Clark, R. G. Assessing avian diets using stable isotopes analysis. I: Turnover of 13C in tissues. The Condor 94, 181–188 (1992).Article 

    Google Scholar 
    54.Hobson, K. A. & Clark, R. G. Assessing avian diets using stable isotopes II: factors influencing diet-tissue fractionation. The Condor 94, 189–197 (1992).Article 

    Google Scholar 
    55.Casey, M. M. & Post, D. M. The problem of isotopic baseline: Reconstructing the diet and trophic position of fossil animals. Earth Sci. Rev. 106, 131–148 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    56.Barnes, C., Seeting, C. J., Jennings, S., Barry, J. T. & Polunin, N. V. C. Effect of temperature and ration size on carbon and nitrogen isotope trophic fractionation. Funct. Ecol. 21, 356–362 (2007).Article 

    Google Scholar 
    57.Bloomfield, A. L., Elsdon, T. S., Walther, B. D. & Gier, E. J. Temperature and diet affect carbon and nitrogen isotopes of fish muscle: can amino acid nitrogen isotopes explain effects?. J. Exp. Mar. Biol. Ecol. 399, 48–59 (2011).CAS 
    Article 

    Google Scholar 
    58.Saporiti, F. et al. Latitudinal changes in the structure of marine food webs in the Southwestern Atlantic Ocean. Mar. Ecol. Prog. Ser. 538, 23–34 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    59.Wells, R. S. & Scott, M. D. Bottlenose dolphin, Tursiops truncatus, common bottlenose dolphin in Encyclopedia of Marine Mammals (eds B. Würsig, J.G.M. Thewissen, & K.M. Kovacs) 118–125 (Academic Press, 2018).60.Natoli, A., Peddemors, V. M. & Hoelzel, A. R. Population structure and speciation in the genus Tursiops based on microsatellite and mitochondrial DNA analyses. J. Evol. Biol. 17, 363–375 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Costa, A. P. B., Rosel, P. E., Daura-Jorge, F. G. & Simões-Lopes, P. C. Offshore and coastal common bottlenose dolphins of the western South Atlantic face-to-face: what the skull and the spine can tell us. Mar. Mamm. Sci. 32, 1433–1457 (2016).Article 

    Google Scholar 
    62.Drago, M. et al. Stable oxygen isotopes reveal habitat use by marine mammals in the Río de la Plata estuary and adjoining Atlantic Ocean. Estuar. Coast. Shelf Sci. 238, 106708 (2020).63.Koen, A. M., Pedraza, S. N., Sciavini, A. C. M., Goodall, R. N. & Crespo, E. A. Stomach contents of false killer whales (Pseudorca crassidens) stranded on the coasts of the strait of Magellan, Tierra del Fuego. Mar. Mamm. Sci. 15, 712–724 (1999).64.Page, C. E. & Cooper, N. Morphological convergence in ‘river dolphin’ skulls. PeerJ 5, e4090 (2017).65.Cohen, J. E., Pimm, S. L., Yodzis, P. & Saldañas, J. Body sizes of animal predators and animal prey in food webs. J. Anim. Ecol. 62, 67–78 (1993).Article 

    Google Scholar 
    66.Cohen, J. E., Jonsson, T. & Carpenter, S. R. Ecological community description using the food web, species abundance, and body size. Proc. Natl. Acad. Sci. U.S.A. 100, 1781–1786 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Warren, P. H. & Lawton, J. H. Invertebrate predator-prey body size relationships: an explanation for upper triangular food webs and patterns in food web structure?. Oecologia 74, 231–235 (1987).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Kerr, S. R. & Dickie, L. M. The biomass spectrum: a predator-prey theory of aquatic production. (Columbia University Press, 2001).69.Leaper, R. & Huxham, M. Size constraints in a real food web: predator, parasite and prey body-size relationships. Oikos 99, 443–456 (2002).Article 

    Google Scholar 
    70.Memmott, J., Martinez, N. D. & J.E., C. Predators, parasitoids and pathogens: species richness, trophic generality and body sizes in a natural food web. J. Anim. Ecol. 69, 1–15 (2000).71.Williams, R. J. & Martinez, N. D. Simple rules yield complex food webs. Nature 404, 180–183 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    72.Jennings, S. Size-based analyses of aquatic food webs in Aquatic food webs: an ecosystem approach (eds A. Belgrano, U.M. Scharler, J. Dunne, & R.E. Ulanowicz) 86–97 (Oxford University Press, 2005).73.Layman, C. A., Winemiller, K. O., Arrington, D. A. & Jepsen, D. B. Body size and trophic position in a diverse tropical food web. Ecology 86, 2530–2535 (2005).Article 

    Google Scholar 
    74.Jeglinski, J., Goetz, K. T., Werner, C., Costa, D. P. & Trillmich, F. Same size – same niche? Foraging niche separation between sympatric juvenile Galapagos sea lions and adult Galapagos fur seals. J. Anim. Ecol. 82, 694–706 (2013).PubMed 
    Article 

    Google Scholar 
    75.Akin, S. & Winemiller, K. O. Body size and trophic position in a temperate estuarine food web. Acta Oecol. 33, 144–153 (2008).ADS 
    Article 

    Google Scholar 
    76.Romanuk, T. N., Hayward, A. & Hutchings, J. A. Trophic level scales positively with body size in fishes. Glob. Ecol. Biogeogr. 20, 231–240 (2011).Article 

    Google Scholar 
    77.Madigan, D. J. et al. Stable isotope analysis challenges wasp-waist food web assumptions in an upwelling pelagic ecosystem. Sci. Rep. 2, 654 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar  More