More stories

  • in

    Spatial and temporal changes in moth assemblages along an altitudinal gradient, Jeju-do island

    Thornton, I. Island Colonization: The Origin and Development of Island Communities (Cambridge University Press, 2007).Book 

    Google Scholar 
    Weigelt, P., Jetz, W. & Kreft, H. Bioclimatic and physical characterization of the world’s islands. Proc. Natl Acad. Sci. 110, 15307–15312 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vitousek, P., Adsersen, H. & Loope, L. Introduction. In Islands: Biological Diversity and Ecosystem Function (eds Vitousek, P. et al.) 1–6 (Berlin, 1995).Chapter 

    Google Scholar 
    Whittaker, R. J. & Fernández-Palacios, J. M. Island Biogeography: Ecology, Evolution, and Conservation (Oxford University Press, 2007).
    Google Scholar 
    Lomolino, M., Brown, J. & Sax, D. Island biogeography theory. In The Theory of Island Biogeography Revisited (eds Losos, J. & Ricklefs, R.) 13–51 (Princeton University Press, 2010).
    Google Scholar 
    Colom, P., Carreras, D. & Stefanescu, C. Long-term monitoring of Menorcan butterfly populations reveals widespread insular biogeographical patterns and negative trends. Biodivers. Conserv. 28, 1837–1851 (2019).Article 

    Google Scholar 
    Preston, F. W. The canonical distribution of commonness and rarity, part II. Ecology 43, 410–432 (1962).Article 

    Google Scholar 
    Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge University Press, 1995).Book 

    Google Scholar 
    Drakare, S., Lennon, J. J. & Hillebrand, H. The imprint of the geographical, evolutionary and ecological context on species–area relationships. Ecol. Lett. 9(2), 215–227 (2006).Article 
    PubMed 

    Google Scholar 
    Field, R. et al. Spatial species-richness gradients across scales: A meta-analysis. J. Biogeogr. 36, 132–147 (2009).Article 

    Google Scholar 
    Brehm, G., Süssenbach, D. & Fiedler, K. Unique elevational diversity patterns of geometrid moths in an Andean montane rainforest. Ecography 26, 456–466 (2003).Article 

    Google Scholar 
    Brehm, G., Colwell, R. K. & Kluge, J. The role of environment and mid-domain effect on moth species richness along a tropical elevational gradient. Glob. Ecol. Biogeogr. 16, 205–219 (2007).Article 

    Google Scholar 
    Beck, J. & Kitching, I. J. Drivers of moth species richness on tropical altitudinal gradients: A cross-regional comparison. Glob. Ecol. Biogeogr. 18, 361–371 (2009).Article 

    Google Scholar 
    Ashton, L. A. et al. Altitudinal patterns of moth diversity in tropical and subtropical A ustralian rainforests. Aust. Ecol. 41, 197–208 (2016).Article 

    Google Scholar 
    Maunsell, S. C. et al. Elevational turnover in the composition of leaf miners and their interactions with host plants in Australian subtropical rainforest. Aust. Ecol. 41, 238–247 (2016).Article 

    Google Scholar 
    McCain, C. M. Global analysis of reptile elevational diversity. Glob. Ecol. Biogeogr. 19, 541–553 (2010).
    Google Scholar 
    Yu, X. D., Lü, L., Luo, T. H. & Zhou, H. Z. Elevational gradient in species richness pattern of epigaeic beetles and underlying mechanisms at east slope of Balang Mountain in Southwestern China. PLoS ONE 8, e69177 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Beck, J. et al. Elevational species richness gradients in a hyperdiverse insect taxon: A global meta-study on geometrid moths. Glob. Ecol. Biogeogr. 26, 412–424 (2017).Article 

    Google Scholar 
    Szewczyk, T. & McCain, C. M. A systematic review of global drivers of ant elevational diversity. PLoS ONE 11, e0155404 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rahbek, C. The elevational gradient of species richness: A uniform pattern?. Ecography 18, 200–205 (1995).Article 

    Google Scholar 
    Vitousek, P. M. Oceanic islands as model systems for ecological studies. J. Biogeogr. 29, 573–582 (2002).Article 

    Google Scholar 
    Kidane, Y. O., Steinbauer, M. J. & Beierkuhnlein, C. Dead end for endemic plant species? A biodiversity hotspot under pressure. Global Ecol. Conserv. 19, e00670 (2019).Article 

    Google Scholar 
    Meyer, W. M. III. et al. Ground-dwelling arthropod communities of a sky island mountain range in Southeastern Arizona, USA: Obtaining a baseline for assessing the effects of climate change. PLoS ONE 10, e0135210 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kong, W. S. Biogeography of Korean plants 335 (Geobook, 2007) (in Korean).
    Google Scholar 
    Kitching, R. L. et al. Moth assemblages as indicators of environmental quality in remnants of upland Australian rain forest. J. Appl. Ecol. 37, 284–297 (2000).Article 

    Google Scholar 
    Froidevaux, J. S., Broyles, M. & Jones, G. Moth responses to sympathetic hedgerow management in temperate farmland. Agric. Ecosyst. Environ. 270, 55–64 (2019).Article 
    PubMed 

    Google Scholar 
    Fox, R. The decline of moths in Great Britain: A review of possible causes. Insect Conserv. Div. 6, 5–19 (2013).Article 

    Google Scholar 
    Keret, N. M., Mutanen, M. J., Orell, M. I., Itämies, J. H. & Välimäki, P. M. Climate change-driven elevational changes among boreal nocturnal moths. Oecologia 192, 1085–1098 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wenzel, M., Schmitt, T., Weitzel, M. & Seitz, A. The severe decline of butterflies on western German calcareous grasslands during the last 30 years: A conservation problem. Biol. Conserv. 128, 542–552 (2006).Article 

    Google Scholar 
    Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).Article 
    PubMed 

    Google Scholar 
    Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).Article 

    Google Scholar 
    Zenker, M. M. et al. Diversity and composition of Arctiinae moth assemblages along elevational and spatial dimensions in Brazilian Atlantic Forest. J. Insect Conserv. 19, 129–140 (2015).Article 

    Google Scholar 
    Brehm, G. & Fiedler, K. Faunal composition of geometrid moths changes with altitude in an Andean montane rain forest. J. Biogeogr. 30, 431–440 (2003).Article 

    Google Scholar 
    McCain, C. M. & Grytnes, J. A. Elevational gradients in species richness. In Encyclopedia of Life Sciences (Wiley, Chichester, 2010).
    Google Scholar 
    Heinrich, B. The Hot-Blooded Insects: Strategies and Mechanisms of Thermoregulation 601 (Harvard University Press, 1993).Book 

    Google Scholar 
    Heinrich, B. Thermoregulation in Endothermic Insects: Body temperature is closely attuned to activity and energy supplies. Science 185, 747–756 (1974).Article 
    PubMed 

    Google Scholar 
    May, M. L. Insect thermoregulation. Annu. Rev. Entomol. 24, 313–349 (1979).Article 

    Google Scholar 
    Heidrich, L. et al. Noctuid and geometrid moth assemblages show divergent elevational gradients in body size and color lightness. Ecography 44, 1169–1179 (2021).Article 
    MathSciNet 

    Google Scholar 
    Holloway, J. D. Macrolepidoptera diversity in the Indo-Australian tropics, geographic, biotopic and taxonomic variations. Biol. J. Linn. Soc. 30, 325–341 (1987).Article 

    Google Scholar 
    Axmacher, J. C. et al. Diversity of geometrid moths (Lepidoptera: Geometridae) along an Afrotropical elevational rainforest transect. Divers. Distrib. 10, 293–302 (2004).Article 

    Google Scholar 
    Heinrich, B. & Mommsen, T. P. Flight of winter moths near 0°C. Science 228, 177–179 (1985).Article 
    PubMed 

    Google Scholar 
    Rydell, J. & Lancaster, W. C. Flight and thermoregulation in moths were shaped by predation from bats. Oikos 88, 13–18 (2000).Article 

    Google Scholar 
    Skou, P. The geometroid moths of North Europe. Entomonograph, Vol. 6. Brill, Leiden. (1986).Zahiri, R. et al. Molecular phylogenetics of Erebidae (Lepidoptera, noctuoidea). Syst. Entomol. 37, 102–124 (2012).Article 

    Google Scholar 
    Fiedler, K., Brehm, G., Hilt, N., Sussenbach, D. & Hauser, C. L. Variation of diversity patterns across moth families along a tropical altitudinal gradient. Ecol. Stud. 198, 167–179 (2008).Article 

    Google Scholar 
    Longino, J. T. & Colwell, R. K. Density compensation, species composition, and richness of ants on a neotropical elevational gradient. Ecosphere 2, 1–20 (2011).Article 

    Google Scholar 
    Beck, J. & Chey, V. K. Explaining the elevational diversity pattern of geometrid moths from Borneo: A test of five hypotheses. J. Biogeogr. 35, 1452–1464 (2008).Article 

    Google Scholar 
    Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219 (2008).Article 
    PubMed 

    Google Scholar 
    Kwon, T. S. Ants foraging on grasses in South Korea: High diversity in Jeju Island and negative correlation with aphids. J. Asia-Pac. Biodivers. 10, 465–471 (2017).Article 

    Google Scholar 
    Han, E. K. et al. A disjunctive marginal edge of evergreen broad-leaved oak (Quercus gilva) in East Asia: The high genetic distinctiveness and unusual diversity of Jeju island populations and insight into a massive, independent postglacial colonization. Genes 11, 1114 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chi, Y., Shi, H., Wang, Y., Guo, Z. & Wang, E. Evaluation on island ecological vulnerability and its spatial heterogeneity. Mar. Pollut. Bull. 125, 216–241 (2017).Article 
    PubMed 

    Google Scholar 
    Vehviläinen, H., Koricheva, J. & Ruohomäki, K. Tree species diversity influences herbivore abundance and damage: Meta-analysis of long-term forest experiments. Oecologia 152, 287–298 (2007).Article 
    PubMed 

    Google Scholar 
    Root, R. B. Organization of plant–arthropod association in simple and diverse habitats: The fauna of collards (I. Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).Article 

    Google Scholar 
    Otway, S. J., Hector, A. & Lawton, J. H. Resource dilution effects on specialist insect herbivores in a grassland biodiversity experiment. J. Anim. Ecol. 74, 234–240 (2005).Article 

    Google Scholar 
    Hawkins, B. A. et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117 (2003).Article 

    Google Scholar 
    Qian, H. Environment–richness relationships for mammals, birds, reptiles, and amphibians at global and regional scales. Ecol. Res. 25, 629–637 (2010).Article 

    Google Scholar 
    Major, J. A climatic index to vascular plant activity. Ecology 44, 485–498 (1963).Article 

    Google Scholar 
    Latham, R. E. & Ricklefs, R. E. Global patterns of tree species richness in moist forests: Energy-diversity theory does not account for variation in species richness. Oikos 67, 325–333 (1993).Article 

    Google Scholar 
    Francis, A. P. & Currie, D. J. A globally consistent richness-climate relationship for angiosperms. Am. Nat. 161, 523–536 (2003).Article 
    PubMed 

    Google Scholar 
    Storch, D. et al. Energy, range dynamics and global species richness patterns: Reconciling mid-domain effects and environmental determinants of avian diversity. Ecol. Lett. 9, 1308–1320 (2006).Article 
    PubMed 

    Google Scholar 
    Intachat, J., Holloway, J. D. & Staines, H. Effects of weather and phenology on the abundance and diversity of geometroid moths in a natural Malaysian tropical rain forest. J. Trop. Ecol. 17, 411–429 (2001).Article 

    Google Scholar 
    Choi, S. W. Effects of weather factors on the abundance and diversity of moths in a temperate deciduous mixed forest of Korea. Zool. Sci. 25, 53–58 (2008).Article 

    Google Scholar 
    Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lennon, J. J., Koleff, P., Greenwood, J. J. D. & Gaston, K. J. The geographical structure of British bird distributions: Diversity, spatial turnover and scale. J. Anim. Ecol. 70, 966–979 (2001).Article 

    Google Scholar 
    Choi, S. W. A high mountain moth assemblage quickly recovers after fire. Ann. Entomol. Soc. Am. 111, 304–311 (2018).
    Google Scholar 
    van Swaay, C., Warren, M. & Loïs, G. Biotope use and trends of European butterflies. J. Insect Conserv. 10, 189–209 (2006).Article 

    Google Scholar 
    De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. Proc. Natl Acad. Sci. 110, 18561–18565 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).Article 
    PubMed 

    Google Scholar 
    Conrad, K. F., Warren, M. S., Fox, R., Parsons, M. S. & Woiwod, I. P. Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biol. Conserv. 132, 279–291 (2006).Article 

    Google Scholar 
    White, E. R. Minimum time required to detect population trends: The need for long-term monitoring programs. Bioscience 69, 40–46 (2019).Article 

    Google Scholar 
    Forister, M. L., Pelton, E. M. & Black, S. H. Declines in insect abundance and diversity: We know enough to act now. Conserv. Sci. Pract. 1, e80 (2019).
    Google Scholar 
    Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Div. 13, 103–114 (2020).Article 

    Google Scholar 
    Kim, J. W., Boo, K. O., Choi, J. T. & Byun, Y. H. Climate Change of 100 Years on the Korean Peninsula (National Institute of Meteorological Science, 2018).
    Google Scholar 
    Kim, S. S., Beljaev, E. A. & Oh, S. H. Illustrated Catalogue of Geometridae in Korea (Lepidoptera: Geometrinae, Ennominae) (Korea Research Institute of Bioscience and Biotechnology & Center for Insect Systematics, 2001).
    Google Scholar 
    Kononenko, V.S., Ahn, S.B. & Ronkay, L. Illustrated catalogue of Noctuidae in Korea (Lepidoptera). Insects of Korea 3. KRIBB & CIS, Junghaengsa (1998).Shin, Y.H. Coloured illustrations of the moths of Korea. Academybook (2001).Kim, S.S., Choi, S.W., Sohn, J.C., Kim, T. & Lee, B.W. The Geometrid moths of Korea (Lepidoptera: Geometridae). Junghaengsa (2016).Kim, C. G. & Kim, N. W. Altitudinal pattern of evapotranspiration and water need for upland crops in Jeju Island. J. Korea Water Resour. Assoc. 48, 915–923 (2015).Article 

    Google Scholar 
    Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

    Google Scholar 
    Magurran, A. E. Ecological Diversity and its Measurement (Princeton University Press, 1988).Book 

    Google Scholar 
    Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

    Google Scholar 
    R Development Core Team. R 4.0.3. R: A language and environment for statistical computing. R Foundation for statistical computing Vienna. Austria. URL http://www.R-project.org. (2020).Pohlert, T. Non-parametric trend tests and change-point detection. R-package version 0.0.1. (2020).Hipel, K. W. & McLeod, A. I. Time Series Modelling of Water Resources and Environmental Systems (Elsevier, 1994).
    Google Scholar 
    Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T. J. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62, 361–371 (2006).Article 
    MathSciNet 
    PubMed 
    MATH 

    Google Scholar 
    Colwell, R. K. Estiamtes, Version 91: Statistical Estimation of Species Richness and Shared Species from Samples (University of Connecticut, 2013).
    Google Scholar 
    Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Global Ecol. Biogeogr. 19, 134–143 (2010).Article 

    Google Scholar 
    Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).Article 

    Google Scholar  More

  • in

    Incorporating distance metrics and temporal trends to refine mixed stock analysis

    MacPherson, E. Ontogenetic shifts in habitat use and aggregation in juvenile sparid fishes. J. Exp. Mar. Bio. Ecol. 220, 127–150 (1998).Article 

    Google Scholar 
    Freitas, C., Olsen, E. M., Knutsen, H., Albretsen, J. & Moland, E. Temperature-associated habitat selection in a cold-water marine fish. J. Anim. Ecol. 85, 628–637 (2016).Article 
    PubMed 

    Google Scholar 
    Michelot, C. et al. Seasonal variation in coastal marine habitat use by the European shag: Insights from fine scale habitat selection modeling and diet. Deep. Res. Part II Top. Stud. Oceanogr. 141, 224–236 (2017).Article 

    Google Scholar 
    Davoren, G. K., Montevecchi, W. A. & Anderson, J. T. Distributional patterns of a marine bird and its prey: Habitat selection based on prey and conspecific behaviour. Mar. Ecol. Prog. Ser. 256, 229–242 (2003).Article 

    Google Scholar 
    Chiarello, A. G. et al. A translocation experiment for the conservation of maned sloths, Bradypus torquatus (Xenarthra, Bradypodidae). Biol. Conserv. 118, 421–430 (2004).Article 

    Google Scholar 
    Fukuda, Y. et al. Environmental resistance and habitat quality influence dispersal of the saltwater crocodile. Mol. Ecol. 31, 1076–1092 (2022).Article 
    PubMed 

    Google Scholar 
    O’Leary, S. J., Dunton, K. J., King, T. L., Frisk, M. G. & Chapman, D. D. Genetic diversity and effective size of Atlantic sturgeon, Acipenser oxyrhinchus oxyrhinchus river spawning populations estimated from the microsatellite genotypes of marine-captured juveniles. Conserv. Genet. 15, 1173–1181 (2014).Article 

    Google Scholar 
    Brüniche-Olsen, A. et al. Genetic data reveal mixed-stock aggregations of gray whales in the North Pacific Ocean. Biol. Lett. 14, 1–4 (2018).Article 

    Google Scholar 
    Carroll, E. L. et al. Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile-Peru wintering grounds and the South Georgia (Islas Georgias Del Sur) feeding ground. J. Hered. 111, 263–276 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bowen, A. B. W. et al. Origin of hawksbill turtles in a Caribbean feeding area as indicated by genetic markers. Ecol. Appl. 6, 566–572 (1996).Article 

    Google Scholar 
    Paxton, K. L., Yau, M., Moore, F. R. & Irwin, D. E. Differential migratory timing of western populations of Wilson’s Warbler (Cardellina pusilla) revealed by mitochondrial DNA and stable isotopes. Auk 130, 689–698 (2013).Article 

    Google Scholar 
    Anderson, E. C., Waples, R. S. & Kalinowski, S. T. An improved method for predicting the accuracy of genetic stock identification. Can. J. Fish. Aquat. Sci. 65, 1475–1486 (2008).Article 

    Google Scholar 
    Debevec, E. M. SPAM (version 3.2): Statistics program for analyzing mixtures. J. Hered. 91, 509–511 (2000).Article 
    PubMed 

    Google Scholar 
    Bolker, B. M., Okuyama, T., Bjorndal, K. A. & Bolten, A. B. Incorporating multiple mixed stocks in mixed stock analysis: ‘Many-to-many’ analyses. Mol. Ecol. 16, 685–695 (2007).Article 
    PubMed 

    Google Scholar 
    Neaves, P. I., Wallace, C. G., Candy, J. R. & Beacham, T. D. CBayes: Computer Program for Mixed Stock Analysis of Allelic Data. Free Program Distributed by the Authors Over the Internet. at (2005).Pella, J. & Masuda, M. Bayesian methods for analysis of stock mixtures from genetic characters. Fish. Bull. 99, 151–167 (2001).
    Google Scholar 
    Bolker, B., Okuyama, T., Bjorndal, K. A. & Bolten, A. B. Sea turtle stock estimation using genetic markers: Accounting for sampling error of rare genotypes. Ecol. Appl. 13, 763–775 (2003).Article 

    Google Scholar 
    Okuyama, T. & Bolker, B. M. Combining genetic and ecological data to estimate sea turtle origins. Ecol. Appl. 15, 315–325 (2005).Article 

    Google Scholar 
    Nishizawa, H. et al. Composition of green turtle feeding aggregations along the Japanese archipelago: Implications for changes in composition with current flow. Mar. Biol. 160, 2671–2685 (2013).Article 

    Google Scholar 
    Naro-Maciel, E. et al. Predicting connectivity of green turtles at Palmyra Atoll, central Pacific: A focus on mtDNA and dispersal modelling. J. R. Soc. Interface 11, 20130888 (2014).Proietti, M. C. et al. Green turtle Chelonia mydas mixed stocks in the western South Atlantic, as revealed by mtDNA haplotypes and drifter trajectories. Mar. Ecol. Prog. Ser. 447, 195–209 (2012).Article 

    Google Scholar 
    van der Zee, J. P. et al. Population recovery changes population composition at a major southern Caribbean juvenile developmental habitat for the green turtle, Chelonia mydas. Sci. Rep. 9, 1–11 (2019).
    Google Scholar 
    Shamblin, B. M. et al. Mexican origins for the Texas green turtle foraging aggregation: A cautionary tale of incomplete baselines and poor marker resolution. J. Exp. Mar. Bio. Ecol. 488, 111–120 (2017).Article 

    Google Scholar 
    Seminoff, J. A. et al. Status Review of the Green Turtle (Chelonia mydas) Under the Endangered Species Act. (NOAA Technical Memorandum, NOAA-NMFS-SWFSC, 2015).Chaloupka, M. et al. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Glob. Ecol. Biogeogr. 17, 297–304 (2008).Article 

    Google Scholar 
    Bjorndal, K. A. & Bolten, A. B. Annual variation in source contributions to a mixed stock: Implications for quantifying connectivity. Mol. Ecol. 17, 2185–2193 (2008).Article 
    PubMed 

    Google Scholar 
    Roland, J., Keyghobadi, N. & Fownes, S. Alpine Parnassius butterfly dispersal: Effects of landscape and population size. Ecology 81, 1642–1653 (2000).Article 

    Google Scholar 
    Vanschoenwinkel, B., De Vries, C., Seaman, M. & Brendonck, L. The role of metacommunity processes in shaping invertebrate rock pool communities along a dispersal gradient. Oikos 116, 1255–1266 (2007).Article 

    Google Scholar 
    Shamblin, B. M. et al. Mitogenomic sequences better resolve stock structure of southern Greater Caribbean green turtle rookeries. Mol. Ecol. 21, 2330–2340 (2012).Article 
    PubMed 

    Google Scholar 
    Witherington, B., Hirama, S. & Hardy, R. Young sea turtles of the pelagic Sargassum-dominated drift community: Habitat use, population density, and threats. Mar. Ecol. Prog. Ser. 463, 1–22 (2012).Article 

    Google Scholar 
    Putman, N. F. & Mansfield, K. L. Direct evidence of swimming demonstrates active dispersal in the sea turtle ‘lost years’. Curr. Biol. 25, 1221–1227 (2015).Article 
    PubMed 

    Google Scholar 
    Mansfield, K. L., Wyneken, J. & Luo, J. First Atlantic satellite tracks of ‘lost years’ green turtles support the importance of the Sargasso Sea as a sea turtle nursery. Proc. R. Soc. B Biol. Sci. 288, 20210057 (2021).Putman, N. F. et al. Predicted distributions and abundances of the sea turtle ‘lost years’ in the western North Atlantic Ocean. Ecography (Cop.) 43, 506–517 (2020).Article 

    Google Scholar 
    Putman, N. F. & Naro-Maciel, E. Finding the ‘lost years’ in green turtles: Insights from ocean circulation models and genetic analysis. Proc. R. Soc. B Biol. Sci. 280, 20131468 (2013).Naro-Maciel, E., Hart, K. M., Cruciata, R. & Putman, N. F. DNA and dispersal models highlight constrained connectivity in a migratory marine megavertebrate. Ecography (Cop.) 40, 586–597 (2017).Article 

    Google Scholar 
    Ehrhart, L. M., Redfoot, W. E. & Bagley, D. A. Marine turtles of the central region of the Indian River Lagoon system, Florida. Florida Sci. 70, 415–434 (2007).
    Google Scholar 
    Redfoot, W. & Ehrhart, L. Trends in size class distribution, recaptures, and abundance of juvenile green turtles (Chelonia mydas) utilizing a rock riprap lined embayment at Port Canaveral, Florida, USA, as developmental habitat. Chelonian Conserv. Biol. 12, 252–261 (2013).Article 

    Google Scholar 
    Ehrhart, L., Redfoot, W., Bagley, D. & Mansfield, K. Long-term trends in loggerhead (Caretta caretta) nesting and reproductive success at an important western Atlantic rookery. Chelonian Conserv. Biol. 13, 173–181 (2014).Article 

    Google Scholar 
    Bolten, A. B. Techniques for measuring sea turtles. in Research and Management Techniques for the Conservation of Sea Turtles. (eds. Eckert, K. L., Bjorndal, K. A., Abreu-Grobois, F. A. & Donnelly, M.). 1–5 (1999).Bagley, D. A. Characterizing Juvenile Green Turtles, (Chelonia mydas), from Three East Central Florida Developmental Habitats. (University of Central Florida, 2003).Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Faircloth, B. & Glenn, T. Preparation of an AMPure XP Substitute. AKA Serapure https://doi.org/10.6079/J9MW2F26 (2016).Article 

    Google Scholar 
    Abreu-Grobois, F. A. et al. New mtDNA Dloop primers which work for a variety of marine turtle species may increase the resolution of mixed stock analyses. in Proceedings of the 26th Annual Symposium on Sea Turtle Biology. 179 (International Sea Turtle Society, 2006).Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

    Google Scholar 
    Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).Article 
    PubMed 

    Google Scholar 
    Wright, S. Evolution and the Genetics of Populations. Vol. 4. Variability Within and Among Natural Populations. (University of Chicago Press, 1978).Hays, G. C. Ocean currents and marine life. Curr. Biol. 27, R470–R473 (2017).Article 
    PubMed 

    Google Scholar 
    Engstrom, T. N., Meylan, P. A. & Meylan, A. B. Origin of juvenile loggerhead turtles (Caretta caretta) in a tropical developmental habitat in Caribbean Panamá. Anim. Conserv. 5, 125–133 (2002).Article 

    Google Scholar 
    Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute, F. W. C. F. W. R. I. Index Nesting Beach Survey (INBS). (2021).Cuevas Flores, E. A., Guzmán Hernández, V., Guerra Santos, J. J. & Rivas Hernández, G. A. El uso del Conocimiento de las Tortugas Marinas Como Herramienta para la Restauración de sus Poblaciones y Hábitats Asociados. (Universidad Autónoma del Carmen, 2019).Pineda, O. G. & Rocha, A. R. B. Las Tortugas Marinas en México: Logros y Perspectivas para su Conservación. (CONANP, 2016).Varela, R. G., Quílez, G. Z. & Harrison, E. Report on the 2014 Green Turtle Program at Tortuguero, Costa Rica. (2015).Azanza Ricardo, J. et al. Nesting ecology of Chelonia mydas (Testudines: Cheloniidae) on the Guanahacabibes Peninsula. Cuba. Rev. Biol. Trop. 61, 1935–1945 (2013).PubMed 

    Google Scholar 
    Nalovic, M. A. et al. Sea Turtles in the North Atlantic & Wider Caribbean Region. (2020).Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).Article 

    Google Scholar 
    Kruschke, J. K. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. https://doi.org/10.1016/B978-0-12-405888-0.09999-2 (Academic Press, 2015).Ruiz-Urquiola, A. et al. Population genetic structure of greater Caribbean green turtles (Chelonia mydas) based on mitochondrial DNA sequences, with an emphasis on rookeries from southwestern Cuba. Rev. Investig. Mar. 31, 33–52 (2010).
    Google Scholar 
    Long, C. A. et al. Incongruent long-term trends of a marine consumer and primary producers in a habitat affected by nutrient pollution. Ecosphere 12, e03553 (2021).Article 

    Google Scholar 
    Phillips, K. F., Stahelin, G. D., Chabot, R. M. & Mansfield, K. L. Long-term trends in marine turtle size at maturity at an important Atlantic rookery. Ecosphere 12, 7 (2021).Article 

    Google Scholar 
    Bjorndal, K. A., Bolten, A. B. & Chaloupka, M. Y. Evaluating trends in abundance of immature green turtles, Chelonia mydas, in the Greater Caribbean. Ecol. Appl. 15, 304–314 (2005).Article 

    Google Scholar 
    Naro-Maciel, E. et al. The interplay of homing and dispersal in green turtles: A focus on the southwestern atlantic. J. Hered. 103, 792–805 (2012).Article 
    PubMed 

    Google Scholar 
    Monzón-Argüello, C. et al. Evidence from genetic and Lagrangian drifter data for transatlantic transport of small juvenile green turtles. J. Biogeogr. 37, 1752–1766 (2010).Article 

    Google Scholar 
    Luke, K., Horrocks, J. A., LeRoux, R. A. & Dutton, P. H. Origins of green turtle (Chelonia mydas) feeding aggregations around Barbados, West Indies. Mar. Biol. 144, 799–805 (2004).Article 

    Google Scholar 
    Bass, A. L., Epperly, S. P. & Braun-McNeill, J. Green turtle (Chelonia mydas) foraging and nesting aggregations in the Caribbean and Atlantic: Impact of currents and behavior on dispersal. J. Hered. 97, 346–354 (2006).Article 
    PubMed 

    Google Scholar 
    Lahanas, P. N. et al. Genetic composition of a green turtle (Chelonia mydas) feeding ground population: Evidence for multiple origins. Mar. Biol. 130, 345–352 (1998).Article 

    Google Scholar 
    Foley, A. M. et al. Characteristics of a green turtle (Chelonia mydas) assemblage in northwestern Florida determined during a hypothermic stunning event. Gulf Mex. Sci. 25, 131–143 (2007).
    Google Scholar 
    Bass, A. L., Lagueux, C. J. & Bowen, B. W. Origin of green turtles, Chelonia mydas, at ‘Sleeping Rocks’ off the Northeast coast of Nicaragua. Copeia 1998, 1064 (1998).Article 

    Google Scholar 
    Bass, A. L. & Witzell, W. N. Demographic composition of immature green turtles (Chelonia mydas) from the East Central Florida Coast: Evidence from mtDNA markers. Herpetologica 56, 357–367 (2000).
    Google Scholar 
    Bjorndal, K. A., Parsons, J., Mustin, W. & Bolten, A. B. Threshold to maturity in a long-lived reptile: Interactions of age, size, and growth. Mar. Biol. 160, 607–616 (2013).Article 

    Google Scholar 
    Perrault, J. R. et al. Maternal health status correlates with nest success of leatherback sea turtles (Dermochelys coriacea) from Florida. PLoS ONE 7, e31841 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Montero, N. et al. Warmer and wetter conditions will reduce offspring production of hawksbill turtles in Brazil under climate change. PLoS ONE 13, 1–16 (2018).Article 

    Google Scholar 
    Shamblin, B. M. et al. Geographic patterns of genetic variation in a broadly distributed marine vertebrate: New insights into loggerhead turtle stock structure from expanded mitochondrial DNA sequences. PLoS ONE 9, 85956 (2014).Article 

    Google Scholar 
    Anderson, J. D., Shaver, D. J. & Karel, W. J. Genetic Diversity and Natal Origins of Green Turtles (Chelonia mydas) in the Western Gulf of Mexico. J. Herpetol. 47, 251–257 (2013).Article 

    Google Scholar  More

  • in

    Eddy covariance-based differences in net ecosystem productivity values and spatial patterns between naturally regenerating forests and planted forests in China

    Differences in environmental factorsEnvironmental factors showed value differences between forest types, while the significance of differences differed among variables, which were both found with corrected values and original measurements (Fig. 1).Figure 1The differences in environmental factors between naturally regenerating forests (NF) and planted forests (PF) in China. The environmental factors include three annual climatic factors (a–c), three seasonal temperature factors (d–f), three seasonal precipitation factors (g–i), three biotic factors (j–l), and two soil factors (m,n). Three annual climatic factors include mean annual air temperature (MAT, a), mean annual precipitation (MAP, b), and aridity index (AI, c) defined as the ratio of MAP to annual potential evapotranspiration. Three seasonal temperature factors include the temperature of the warmest month (Tw, d), the temperature of the coldest month (Tc, e), temperature annual range (TR, f). Three seasonal precipitation factors include precipitation of the wettest month (Pw, g), precipitation of the driest month (Pd, h), and precipitation seasonality (Ps, i) defined as the standard deviation of monthly precipitation during the measuring year. Three biological factors include the mean annual leaf area index (LAI, j), the maximum leaf area index (MLAI, k), and stand age (SA, l). Two soil factors include soil organic carbon content (SOC, m) and soil total nitrogen content (STN, n). The differences are tested for each variable with one-way analysis of variance (ANOVA), where * and ** indicate significant differences between forest types at significance levels of α = 0.05 and α = 0.01, respectively. The corrected values are mean values during 2003–2019 after correcting the original measurements with the interannual trend (See methods), which are listed in each panel, while original measurements are mean values during the measuring period of each ecosystem, which are not shown in each panel.Full size imageFor annual climatic factors, the significant difference between NF and PF only appeared in MAT (Fig. 1a). The mean MAT of NF was 10.50 ± 7.81 °C, which was significantly lower than that of PF (15.65 ± 6.23 °C) (p  0.05) (Fig. 2c). Even considering the significant effects of MAT on ER, ANCOVA results obtained by fixing MAT as a covariant also suggested that ER values did not significantly differ between forest types (F = 0.01, p  > 0.05). Fixing other variables as a covariant also drew a similar result.Therefore, NF showed a lower NEP resulting from the lower GPP than PF, while their differences were not statistically significant (Fig. 2).Differences in NEP latitudinal patternsCarbon fluxes showed divergent latitudinal patterns between NF and PF, while their latitudinal patterns varied among carbon fluxes, which were both found with corrected values and original measurements (Fig. 3).Figure 3The latitudinal patterns of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  0.05).The ER of NF showed a significant decreasing latitudinal pattern (Fig. 3e), while that of PF exhibited no significant latitudinal pattern (Fig. 3f). The increasing latitude caused the ER of NF to significantly decrease. Each unit increase in latitude led to a 28.71 gC m−2 year−1 decrease in ER, with an R2 of 0.31. However, the increasing latitude contributed little to the ER spatial variation of PF (p  > 0.05).In addition, the latitudinal patterns of carbon fluxes and their differences between forest types were also obtained with the original measurements (Fig. 3, grey points). The latitudinal patterns of random error adding carbon fluxes were comparable to those of our corrected carbon fluxes (Fig. 3), which confirmed that the latitudinal patterns of carbon fluxes and their differences between forest types would not be affected by the uncertainties in generating the corrected carbon fluxes.Therefore, among NFs, the similar decreasing latitudinal patterns of GPP and ER meant that NEP showed no significant latitudinal pattern, while the significant decreasing latitudinal pattern of GPP and no significant latitudinal pattern of ER caused NEP to show a decreasing latitudinal pattern among PFs.Differences in the environmental effects on NEP spatial variationsEnvironmental factors, including the annual climatic factors, seasonal temperature factors, seasonal precipitation factors, biological factors, and soil factors, exerted divergent effects on the spatial variations of NEP and its components, which also differed between forest types (Table 1). No factor was found to affect that the spatial variation of NEP among NFs, while most annual and seasonal climatic factors were found to affect that among PFs. The spatial variations of GPP and ER among NFs were both affected by most annual and seasonal climatic factors and LAI, while those among PFs were primarily shaped by most annual and seasonal climatic factors. Though LAI showed no significant effect on GPP and ER spatial variations among PFs, SA exerted a significant negative effect. In addition, the spatial variations of soil variables contributed little to the spatial variations of carbon fluxes. Therefore, among NFs, most annual and seasonal climatic factors and LAI were found to affect GPP and ER spatial variations, while no factor was found to significantly influent the NEP spatial variation. However, among PFs, most annual and seasonal climatic factors were found to affect the spatial variations of NEP and its components, while LAI showed no significant effect. Using the original measurements also generated the similar correlation coefficients (Supplementary Table S1).Table 1 Correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF).Full size tableGiven the high correlations among annual climatic factors and seasonal climatic factors (Supplementary Table S2), the partial correlation analysis was applied to determine which factors should be employed to reveal the mechanisms underlying the spatial variations of NEP. Partial correlation analysis showed that MAT and MAP exerted the most important roles in spatial variations of NEP and its components (Table 2). After controlling MAT (or MAP), other factors seldom showed significant correlation with carbon fluxes, especially fixing MAT (Table 2). In addition, MAT and MAP exerted similar effects on the spatial variations of NEP and its components (Table 1). Using the original measurements also generated the similar partial correlation coefficients (Supplementary Table S3). Therefore, we only presented the effects of MAT on carbon flux spatial variations and their differences between forest types in detail.Table 2 Partial correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF) with fixing mean annual air temperature (MAT) or mean annual precipitation (MAP).Full size tableThe increasing MAT increased carbon fluxes, while the increasing rates differed between forest types (Fig. 4). The increasing MAT contributed little to the NEP spatial variation of NF but raised the NEP of PF (Fig. 4a,b). Each unit increase in MAT caused the NEP of PF to increase at a rate of 27.77 gC m−2 year−1, with an R2 of 0.31 (Fig. 4b). The increasing MAT significantly raised GPP in NF and PF (Fig. 4c,d). For NF, each unit increase in MAT increased GPP at a rate of 43.76 gC m−2 year−1, with an R2 of 0.49 (Fig. 4c), while each unit increase in MAT increased the GPP of PF at a rate of 69.18 gC m−2 year−1, with an R2 of 0.57 (Fig. 4d). The GPP increasing rates did not significantly differ between NF and PF (F = 1.52, p  > 0.05). The increasing MAT also raised ER in both NF and PF (Fig. 4e,f), whose increasing rates were 38.97 gC m−2 year−1 (Fig. 4e) and 36.79 gC m−2 year−1 (Fig. 4f), respectively, while their differences were not statistically significant (F = 0.01, p  > 0.05). In addition, using the original measurements also generated the similar spatial variations and their differences between forest types (Fig. 4). Furthermore, the random error adding carbon fluxes responded similarly to those of our correcting carbon fluxes (Fig. 4), indicating that the effects of MAT on carbon fluxes would not be affected by the uncertainties in our correcting carbon fluxes. Therefore, the similar responses of GPP and ER to MAT made MAT contribute little to NEP spatial variations among NFs, while GPP and ER showed divergent response rates to MAT, which made NEP increase with MAT among PFs.Figure 4The effects of mean annual air temperature (MAT) on the spatial variations of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  More

  • in

    Long-term, basin-scale salinity impacts from desalination in the Arabian/Persian Gulf

    Al-Mutawa, A. M., Al Murbati, W. M., Al Ruwaili, N. A., Al Orafi, A. S., Al Orafi, A., Al Arafati, A., Nasrullah, A., Al Bahow, M. R., Al Anzi, S. M., Rashisi, M. & Al Moosa, S. Z. Desalination in the gcc. the history, the present & the future. Available from: https://www.gcc-sg.org/en-us/CognitiveSources/DigitalLibrary/Lists/DigitalLibrary/WaterandElectricity/1414489603.pdf Second edition, The Cooperation Council for the Arab States of the Gulf (GCC) General Secretariat (2014).Global Water Intelligence. DesalData. https://www.desaldata.com/. Accessed 2022-05-01 (2022).Sharifinia, M., Afshari Bahmanbeigloo, Z., Smith Jr, W. O., Yap, C. K. & Keshavarzifard, M. Prevention is better than cure: Persian gulf biodiversity vulnerability to the impacts of desalination plants. Glob. Change Biol. 25(12), 4022–4033 (2019).Article 

    Google Scholar 
    Connor, R. The United Nations World Water Development Report 2015: Water for a Sustainable World. Number 79. UNESCO, (2015).Al-Senafy, M., Al-Fahad, K. & Hadi, K. Water management strategies in the Arabian gulf countries. In Developments in Water Science, volume 50, pages 221–224. Elsevier, (2003).Ulrichsen, K.C.. Internal and external security in the arab gulf states. Middle East Policy16(2), 39 (2009).Verner, D. Adaptation to a changing climate in the Arab countries: a case for adaptation governance and leadership in building climate resilience. Number 79. World Bank Publications, (2012).Einav, R., Harussi, K. & Perry, D. The footprint of the desalination processes on the environment. Desalination 152(1–3), 141–154 (2003).Article 

    Google Scholar 
    Dawoud, M. A. Environmental impacts of seawater desalination: Arabian Gulf case study. Int. J. Environ. Sustain.1(3) (2012).Chow, A. C. et al. Numerical prediction of background buildup of salinity due to desalination brine discharges into the Northern Arabian Gulf. Water 11(11), 2284 (2019).Article 

    Google Scholar 
    Lee, K. & Jepson, W. Environmental impact of desalination: A systematic review of life cycle assessment. Desalination 509, 115066 (2021).Article 

    Google Scholar 
    Hosseini, H. et al. Marine health of the Arabian gulf: Drivers of pollution and assessment approaches focusing on desalination activities. Mar. Pollut. Bull. 164, 112085 (2021).Article 
    PubMed 

    Google Scholar 
    Le Quesne, W. J. F. et al. Is the development of desalination compatible with sustainable development of the Arabian Gulf?. Mar. Pollut. Bull. 173, 112940 (2021).Article 
    PubMed 

    Google Scholar 
    Kress, N., & Galil, B. Impact of seawater desalination by reverse osmosis on the marine environment. Efficient Desalination by Reverse Osmosis: A guide to RO practice. IWA, London, UK, pp. 177–202 (2015).Reynolds, R. M. Physical oceanography of the Gulf, Strait of Hormuz, and the Gulf of Oman: Results from the Mt Mitchell expedition. Mar. Pollut. Bull. 27, 35–59 (1993).Article 

    Google Scholar 
    Swift, S. A. & Bower, A. S. Formation and circulation of dense water in the Persian/Arabian Gulf. J. Geophys. Res. Oceans 108(C1), 1–4 (2003).Article 

    Google Scholar 
    Pous, S. P., Carton, X., & Lazure, P. Hydrology and circulation in the strait of hormuz and the Gulf of Oman: Results from the gogp99 experiment: 1. strait of hormuz. J. Geophys. Res. Oceans109(C12), (2004).Pous, S., Lazure, P. & Carton, X. A model of the general circulation in the persian gulf and in the strait of hormuz: Intraseasonal to interannual variability. Cont. Shelf Res. 94, 55–70 (2015).Article 

    Google Scholar 
    Johns, W. E., Yao, F., Olson, D. B., Josey, S. A., Grist, J. P. & Smeed, D. A. Observations of seasonal exchange through the Straits of Hormuz and the inferred heat and freshwater budgets of the Persian Gulf. J. Geophys. Res. Oceans108(C12) (2003).Hassanzadeh, S., Hosseinibalam, F. & Rezaei-Latifi, A. Numerical modelling of salinity variations due to wind and thermohaline forcing in the Persian gulf. Appl. Math. Model. 35(3), 1512–1537 (2011).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Price, A. R. G. Western Arabian gulf echinoderms in high salinity waters and the occurrence of dwarfism. J. Nat. Hist. 16(4), 519–527 (1982).Article 

    Google Scholar 
    Sheppard, C. R. C. Similar trends, different causes: Responses of corals to stressed environments in Arabian seas. In Proceedings of the 6th International Coral Reef Symposium Townsville, Australia, volume 3, pp. 297–302 (1988).Coles, S. L. & Jokiel, P. L. Effects of salinity on coral reefs. In Connell, D. W., & Hawker, D. W. editors, Pollution in tropical aquatic systems, pp. 147–166. CRC Press, Florida (1992).Coles, S. L. Coral species diversity and environmental factors in the Arabian gulf and the Gulf of Oman: A comparison to the Indo-Pacific region. Atoll Res. Bull. (2003).D’Agostino, D. et al. Growth impacts in a changing ocean: Insights from two coral reef fishes in an extreme environment. Coral Reefs 40(2), 433–446 (2021).Article 

    Google Scholar 
    Bœuf, G. & Payan, P. How should salinity influence fish growth?. Compar. Biochem. Physiol. Part C Toxicol. Pharmacol. 130(4), 411–423 (2001).Article 

    Google Scholar 
    Baudron, A. R., Needle, C. L., Rijnsdorp, A. D. & Marshall, C. T. Warming temperatures and smaller body sizes: Synchronous changes in growth of north sea fishes. Glob. Change Biol. 20(4), 1023–1031 (2014).Article 

    Google Scholar 
    Dore, M. H. I. Forecasting the economic costs of desalination technology. Desalination 172(3), 207–214 (2005).Article 

    Google Scholar 
    Karagiannis, I. C. & Soldatos, P. G. Water desalination cost literature: Review and assessment. Desalination 223(1–3), 448–456 (2008).Article 

    Google Scholar 
    Al Barwani, H. H. & Purnama, A. Evaluating the effect of producing desalinated seawater on hypersaline Arabian Gulf. Eur. J. Sci. Res. 22(2), 279–285 (2008).
    Google Scholar 
    Lee, W. & Kaihatu, J. M. Effects of desalination on hydrodynamic process in Persian Gulf. Coast. Eng. Proc. 36, 3–3 (2018).Article 

    Google Scholar 
    Ibrahim, H. D. & Eltahir, E. A. B. Impact of brine discharge from seawater desalination plants on Persian/Arabian gulf salinity. J. Environ. Eng. 145(12), 04019084 (2019).Article 

    Google Scholar 
    Campos, E. J. D. et al. Impacts of brine disposal from water desalination plants on the physical environment in the Persian/Arabian Gulf. Environ. Res. Commun. 2(12), 125003 (2020).Article 

    Google Scholar 
    Ibrahim, H. D., Xue, P. & Eltahir, E. A. B. Multiple salinity equilibria and resilience of Persian/Arabian Gulf basin salinity to brine discharge. Front. Mar. Sci. 7, 573 (2020).Article 

    Google Scholar 
    Ibrahim, H. D. Simulated effects of seawater desalination on Persian/Arabian Gulf exchange flow. J. Environ. Eng. 148(4), 04022012 (2022).Article 

    Google Scholar 
    Purnama, A. Assessing the environmental impacts of seawater desalination on the hypersalinity of arabian/persian gulf. In The Arabian Seas: Biodiversity, Environmental Challenges and Conservation Measures, pp. 1229–1245. Springer, (2021).GEBCO Compilation Group. The GEBCO_2021 grid: A continuous terrain model of the global oceans and land, (2021).Stommel, H. Thermohaline convection with two stable regimes of flow. Tellus 13(2), 224–230 (1961).Article 

    Google Scholar 
    Nakamura, M., Stone, P. H. & Marotzke, J. Destabilization of the thermohaline circulation by atmospheric eddy transports. J. Clim. 7(12), 1870–1882 (1994).Article 

    Google Scholar 
    Pasquero, C. & Tziperman, E. Effects of a wind-driven gyre on thermohaline circulation variability. J. Phys. Oceanogr. 34(4), 805–816 (2004).Article 

    Google Scholar 
    Lucarini, V. & Stone, P. H. Thermohaline circulation stability: A box model study. part ii: coupled atmosphere-ocean model. J. Clim. 18(4), 514–529 (2005).Article 

    Google Scholar 
    Wunsch, C. Thermohaline loops, stommel box models, and the sandström theorem. Tellus A Dyn. Meteorol. Oceanogr. 57(1), 84–99 (2005).
    Google Scholar 
    Privett, D. W. Monthly charts of evaporation from the N. Indian Ocean (including the Red Sea and the Persian Gulf). Q. J. R. Meteorol. Soc. 85(366), 424–428 (1959).Article 

    Google Scholar 
    Chao, S.-Y., Kao, T. W. & Al-Hajri, K. R. A numerical investigation of circulation in the Arabian Gulf. J. Geophys. Res. Oceans 97(C7), 11219–11236 (1992).Article 

    Google Scholar 
    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146(730), 1999–2049 (2020).Article 

    Google Scholar 
    Thoppil, P. G. & Hogan, P. J. Persian Gulf response to a wintertime shamal wind event. Deep Sea Res. Part I 57(8), 946–955 (2010).Article 

    Google Scholar 
    Paparella, F., Chenhao, X., Vaughan, G. O. & Burt, J. A. Coral bleaching in the Persian/Arabian Gulf is modulated by summer winds. Front. Mar. Sci. 6, 205 (2019).Article 

    Google Scholar 
    Gutiérrez, J.M., Jones, R. G., Narisma, G.T., Alves, L.M., Amjad, M., Gorodetskaya, I.V., Grose, M., Klutse, N.A.B., Krakovska, S., Li, J., Martínez-Castro, D., Mearns, L.O., Mernild, S.H., Ngo-Duc, T., van den Hurk, B. & Yoon, J.-H. Atlas. In V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, editors, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, (2021). Available from http://interactive-atlas.ipcc.ch/.Alosairi, Y., Imberger, J., & Falconer, R. A. Mixing and flushing in the Persian Gulf (Arabian Gulf). J. Geophys. Res. Oceans116(C3) (2011).Whitehead, J. A. Internal hydraulic control in rotating fluids – applications to oceans. Geophys. Astrophys. Fluid Dyn. 48(1–3), 169–192 (1989).Article 
    MATH 

    Google Scholar 
    Dougherty, W. W., Yates, D. N., Pereira, J. E., Monaghan, A., Steinhoff, D., Ferrero, B., Wainer, I., Flores-Lopez, F., Galaitsi, S., & Kucera, P., et al. The energy–water–health nexus under climate change in the united arab emirates: Impacts and implications. In Climate Change and Energy Dynamics in the Middle East, pp. 131–180. Springer, (2019).Al-Shehhi, M. R., Song, H., Scott, J. & Marshall, J. Water mass transformation and overturning circulation in the Arabian gulf. J. Phys. Oceanogr. 51(11), 3513–3527 (2021).
    Google Scholar 
    Hausfather, Z. & Peters, G. P. Emissions-the “business as usual’’ story is misleading. Nature 577, 618–620 (2020).Article 
    PubMed 

    Google Scholar 
    Al-Ghouti, M. A., Al-Kaabi, M. A., Ashfaq, M. Y. & Da’na, D. A. Produced water characteristics, treatment and reuse: A review. J. Water Process Eng. 28, 222–239 (2019).Article 

    Google Scholar 
    Riegl, B. M. & Purkis, S. J. Coral reefs of the gulf: adaptation to climatic extremes in the world’s hottest sea. In Coral reefs of the Gulf, pp. 1–4. Springer, (2012).Burt, J. A. et al. Insights from extreme coral reefs in a changing world. Coral Reefs 39(3), 495–507 (2020).Article 

    Google Scholar 
    D’Agostino, D. et al. The influence of thermal extremes on coral reef fish behaviour in the Arabian/Persian gulf. Coral Reefs 39(3), 733–744 (2020).Article 

    Google Scholar 
    Lachkar, Z., Mehari, M., Lévy, M., Paparella, F., & Burt, J.A. Recent expansion and intensification of hypoxia in the Arabian gulf and its drivers. Front. Mar. Sci. 1616 (2022).De Verneil, A., Burt, J. A., Mitchell, M., & Paparella, F. Summer oxygen dynamics on a southern Arabian Gulf coral reef. Front. Mar. Sci. 1676 (2021).Petersen, K. L. et al. Impact of brine and antiscalants on reef-building corals in the gulf of aqaba-potential effects from desalination plants. Water Res. 144, 183–191 (2018).Article 
    PubMed 

    Google Scholar 
    Sanchez-Lizaso, J. L. et al. Salinity tolerance of the mediterranean seagrass posidonia oceanica: recommendations to minimize the impact of brine discharges from desalination plants. Desalination 221(1–3), 602–607 (2008).Article 

    Google Scholar 
    Cambridge, M. L., Zavala-Perez, A., Cawthray, G. R., Mondon, J. & Kendrick, G. A. Effects of high salinity from desalination brine on growth, photosynthesis, water relations and osmolyte concentrations of seagrass posidonia australis. Mar. Pollut. Bull. 115(1–2), 252–260 (2017).Article 
    PubMed 

    Google Scholar 
    Cambridge, M. L. et al. Effects of desalination brine and seawater with the same elevated salinity on growth, physiology and seedling development of the seagrass posidonia australis. Mar. Pollut. Bull. 140, 462–471 (2019).Article 
    PubMed 

    Google Scholar 
    Kelaher, B. P., Clark, G. F., Johnston, E. L. & Coleman, M. A. Effect of desalination discharge on the abundance and diversity of reef fishes. Environ. Sci. Technol. 54(2), 735–744 (2019).Article 
    PubMed 

    Google Scholar 
    Gegner, H. M. et al. High salinity conveys thermotolerance in the coral model aiptasia. Biol. Open 6(12), 1943–1948 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Ochsenkühn, M. A., Röthig, T., D’Angelo, C., Wiedenmann, J. & Voolstra, C. R. The role of floridoside in osmoadaptation of coral-associated algal endosymbionts to high-salinity conditions. Sci. Adv. 3(8), e1602047 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gegner, H. M. et al. High levels of floridoside at high salinity link osmoadaptation with bleaching susceptibility in the cnidarian-algal endosymbiosis. Biol. Open 8(12), bio045591 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thoppil, P. G. & Hogan, P. J. A modeling study of circulation and eddies in the Persian Gulf. J. Phys. Oceanogr. 40(9), 2122–2134 (2010).Article 

    Google Scholar 
    Pous, S., Carton, X. & Lazure, P. A process study of the tidal circulation in the Persian gulf. Open J. Mar. Sci. 2(04), 131–140 (2012).Article 

    Google Scholar 
    Haney, R. L. Surface thermal boundary condition for ocean circulation models. J. Phys. Oceanogr. 1(4), 241–248 (1971).Article 

    Google Scholar  More

  • in

    Field research stations are key to global conservation targets

    A theme is emerging in this year’s United Nations conferences on biodiversity (COP15), climate change (COP27) and the international wildlife trade (COP19): countries are struggling to meet key conservation targets. We argue that field research stations are an effective — but imperilled and overlooked — tool that can help policy frameworks to meet those targets. We write on behalf of 149 experts from 47 countries.
    Competing Interests
    The authors declare no competing interests. More

  • in

    Biodiversity loss and climate extremes — study the feedbacks

    As humans warm the planet, biodiversity is plummeting. These two global crises are connected in multiple ways. But the details of the intricate feedback loops between biodiversity decline and climate change are astonishingly under-studied.It is well known that climate extremes such as droughts and heatwaves can have devastating impacts on ecosystems and, in turn, that degraded ecosystems have a reduced capacity to protect humanity against the social and physical impacts of such events. Yet only a few such relationships have been probed in detail. Even less well known is whether biodiversity-depleted ecosystems will also have a negative effect on climate, provoking or exacerbating weather extremes.For us, a group of researchers living and working mainly in Central Europe, the wake-up call was the sequence of heatwaves of 2018, 2019 and 2022. It felt unreal to watch a floodplain forest suffer drought stress in Leipzig, Germany. Across Germany, more than 380,000 hectares of trees have now been damaged (see go.nature.com/3etrrnp; in German), and the forestry sector is struggling with how to plan restoration activities over the coming decades1. What could have protected these ecosystems against such extremes? And how will the resultant damage further impact our climate?
    Nature-based solutions can help cool the planet — if we act now
    In June 2021, the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) published their first joint report2, acknowledging the need for more collaborative work between these two domains. And some good policy moves are afoot: the new EU Forest Strategy for 2030, released in July 2021, and other high-level policy initiatives by the European Commission, formally recognize the multifunctional value of forests, including their role in regulating atmospheric processes and climate. But much more remains to be done.To thoroughly quantify the risk that lies ahead, ecologists, climate scientists, remote-sensing experts, modellers and data scientists need to work together. The upcoming meeting of the United Nations Convention on Biological Diversity in Montreal, Canada, in December is a good opportunity to catalyse such collaboration.Buffers and responsesWhen lamenting the decline in biodiversity, most people think first about the tragedy of species driven to extinction. There are more subtle changes under way, too.For instance, a study across Germany showed that over the past century, most plant species have declined in cover, with only a few increasing in abundance3. Also affected is species functionality4 — genetic diversity, and the diversity of form and structure that can make communities more or less efficient at taking up nutrients, resisting heat or surviving pathogen attacks.When entire ecosystems are transformed, their functionality is often degraded. They are left with less capacity to absorb pollution, store carbon dioxide, soak up water, regulate temperature and support vital functions for other organisms, including humans5. Conversely, higher levels of functional biodiversity increase the odds of an ecosystem coping with unexpected events, including climate extremes. This is known as the insurance effect6.The effect is well documented in field experiments and modelling studies. And there is mounting evidence of it in ecosystem responses to natural events. A global synthesis of various drought conditions showed, for instance, that forests were more resilient when trees with a greater diversity of strategies for using and transporting water lived together7.

    Dead trees near Iserlohn, Germany, in April 2020 (left) and after felling in June 2021 (right).Credit: Ina Fassbender/AFP via Getty

    However, biodiversity cannot protect all ecosystems against all kinds of impacts. In a study this year across plots in the United States and Canada, for example, mortality was shown to be higher in diverse forest ecosystems8. The proposed explanation for this unexpected result was that greater biodiversity could also foster more competition for resources. When extreme events induce stress, resources can become scarce in areas with high biomass and competition can suddenly drive mortality, overwhelming the benefits of cohabitation. Whether or not higher biodiversity protects an ecosystem from an extreme is highly site-specific.Some plants respond to drought by reducing photosynthesis and transpiration immediately; others can maintain business as usual for much longer, stabilizing the response of the ecosystem as a whole. So the exact response of ecosystems to extremes depends on interactions between the type of event, plant strategies, vegetation composition and structure.Which plant strategies will prevail is hard to predict and highly dependent on the duration and severity of the climatic extreme, and on previous extremes9. Researchers cannot fully explain why some forests, tree species or individual plants survive in certain regions hit by extreme climate conditions, whereas entire stands disappear elsewhere10. One study of beech trees in Germany showed that survival chances had a genomic basis11, yet it is not clear whether the genetic variability present in forests will be sufficient to cope with future conditions.And it can take years for ecosystem impacts to play out. The effects of the two consecutive hot drought years, 2018 and 2019, were an eye-opener for many of us. In Leipzig, tree growth declined, pathogens proliferated and ash and maple trees died. The double blow, interrupted by a mild winter, on top of the long-term loss of soil moisture, led to trees dying at 4–20 times the usual rate throughout Germany, depending on the species (see go.nature.com/3etrrnp; in German). The devastation peaked in 2020.Ecosystem changes can also affect atmospheric conditions and climate. Notably, land-use change can alter the brightness (albedo) of the planet’s surface and its capacity for heat exchange. But there are more-complex mechanisms of influence.Vegetation can be a source or sink for atmospheric substances. A study published in 2020 showed that vegetation under stress is less capable of removing ozone than are unstressed plants, leading to higher levels of air pollution12. Pollen and other biogenic particles emitted from certain plants can induce the freezing of supercooled cloud droplets, allowing ice in clouds to form at much warmer temperatures13, with consequences for rainfall14. Changes to species composition and stress can alter the dynamics of these particle emissions. Plant stress also modifies the emission of biogenic volatile organic gases, which can form secondary particles. Wildfires — enhanced by drought and monocultures — affect clouds, weather and climate through the emission of greenhouse gases and smoke particles. Satellite data show that afforestation can boost the formation of low-level, cooling cloud cover15 by enhancing the supply of water to the atmosphere.Research prioritiesAn important question is whether there is a feedback loop: will more intense, and more frequent, extremes accelerate the degradation and homogenization of ecosystems, which then, in turn, promote further climate extremes? So far, we don’t know.One reason for this lack of knowledge is that research has so far been selective: most studies have focused on the impacts of droughts and heatwaves on ecosystems. Relatively little is known about the impacts of other kinds of extremes, such as a ‘false spring’ caused by an early-season bout of warm weather, a late spring frost, heavy rainfall events, ozone maxima, or exposure to high levels of solar radiation during dry, cloudless weather.Researchers have no overview, much less a global catalogue, of how each dimension of biodiversity interacts with the full breadth of climate extremes in different combinations and at multiple scales. In an ideal world, scientists would know, for example, how the variation in canopy density, vegetation age, and species diversity protects against storm damage; and whether and how the diversity of canopy structures controls atmospheric processes such as cloud formation in the wake of extremes. Researchers need to link spatiotemporal patterns of biodiversity with the responses of ecosystem processes to climate extremes.
    Biodiversity needs every tool in the box: use OECMs
    Creating such a catalogue is a huge challenge, particularly given the more frequent occurrence of extremes with little or no precedent16. Scientists will also need to account for the increasing likelihood of pile-ups of climate stressors. The ways in which ecosystems respond to compound events17 could be quite different. Researchers will have to study which facets of biodiversity (genetic, physiological, structural) are required to stabilize ecosystems and their functions against these onslaughts.There is at least one piece of good news: tools for data collection and analysis are improving fast, with huge advances over the past decade in satellite-based observations for both climate and biodiversity monitoring. The European Copernicus Earth-observation programme, for example — which includes the Sentinel 1 and 2 satellite fleet, and other recently launched missions that cover the most important wavelengths of the electromagnetic spectrum — offer metre-scale resolution observations of the biochemical status of plants and canopy structure. Atmospheric states are recorded in unprecedented detail, vertically and in time.Scientists must now make these data interoperable and integrate them with in situ observations. The latter is challenging. On the ground, a new generation of data are being collected by researchers and by citizen scientists18. For example, unique insights into plant responses to stress are coming from time-lapse photography of leaf orientation; accelerometer measures of movement patterns of stems have been shown to provide proxies for the drought stress of trees19.High-quality models are needed to turn these data into predictions. The development of functional ‘digital twins’ of the climate system is now in reach. These models replicate hydrometeorological processes at the metre scale, and are fast enough to allow for rapid scenario development and testing20. The analogous models for ecosystems are still in a more conceptual phase. Artificial-intelligence methods will be key here, to study links between climate extremes and biodiversity.Researchers can no longer afford to track global transformations of the Earth system in disciplinary silos. Instead, ecologists and climate scientists need to establish a joint agenda, so that humanity is properly forewarned: of the risks of removing biodiversity buffers against climate extremes, and of the risk of thereby amplifying these extremes. More

  • in

    COP15 biodiversity plan risks being alarmingly diluted

    I was filled with hope when I read the first draft of the Global Biodiversity Framework (GBF) in mid-2021. It seemed that the parties to the United Nations Convention on Biodiversity had learnt from bitter experience — the failure of the Aichi Biodiversity Targets, set for the previous decade. Instead of vague aims, the draft framework incorporated most of the advice that the scientific community, myself included, had marshalled. It contained ambitious quantitative thresholds, such as those for the area of ecosystem to be protected, the percentage of genetic diversity to be maintained, and percentage reductions for overall extinction rates, pesticide use and subsidies harmful to biodiversity.Then came the square brackets. In the world of policy, these mark proposed amendments that the parties do not yet agree on. The square brackets proliferated at an alarming rate throughout the GBF text, enclosing, neutralizing and paralysing goals and targets. By July 2021, in a version about 10,200 words long, there were more than 900 pairs of square brackets.Brackets germinated with particular vigour in sections that could make the greatest difference for a better future because of their precision, ambition or conceptual novelty. Almost all quantitative thresholds had been bracketed or had disappeared.
    The United Nations must get its new biodiversity targets right
    I applaud the new prominence given to gender justice (with a new dedicated Target 22) and to financial resources and capacity building (Target 19). I wonder why other key aspects have not received the same treatment, and have instead been compressed almost beyond recognition. For example, the first draft highlighted that species, ecosystems, genetic diversity and nature’s contribution to people each needed their own specific, verifiable outcomes. Now they have coagulated into one vague yet verbose paragraph.This thicket of square brackets smothers the GBF and the hopes of those of us who see transformative change as the only way forward for life on Earth as we know it.In a titanic effort, a streamlined proposal from the Informal Group on the GBF has halved the brackets to be considered by the parties when they meet in Montreal, Canada, for the 15th Conference of the Parties (COP15) on 7–19 December.We need a text with teeth — and far fewer brackets. This much we have learnt in the 30 years since the foundational 1992 Rio Summit drew attention to the impact of human activities on the environment: a strong, precise, ambitious text does not in itself ensure successful implementation, but a weak, vague, toothless text almost guarantees failure.It was no surprise when the Convention on Biological Diversity officially declared the failure of its ten-year Aichi Targets. People involved at the international interface of biodiversity science and policy were already discussing how to do better in the next decade with the GBF.
    Crucial biodiversity summit will go ahead in Canada, not China: what scientists think
    The scientific community rose to the occasion. In just three years, we produced the first-ever intergovernmental appraisal of life on Earth and what it means to people: The Global Assessment Report on Biodiversity and Ecosystem Services from IPBES (the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services), which I co-chaired. It was ready in time for the original 2020 date for COP15, before the global disruption caused by COVID-19. It was the most comprehensive ever synthesis of published information on the topic, an inclusive conceptual framework involving various disciplines and knowledge systems, and unprecedented participation of Indigenous peoples.Then, in 2020, we assembled an interdisciplinary team of more than 60 biodiversity scientists across the world, and within a few months produced detailed suggestions for the goals of the GBF. Since then, we have made the best of the many pandemic postponements by issuing a stream of specific, evidence-based recommendations on targets, scenarios and implementation.The scientific advice is convergent. First, the GBF needs to explicitly address each facet of biodiversity; none is a good substitute or umbrella for the others. Second, the biodiversity goals must be more ambitious than ever, accompanied by equally ambitious targets for concrete action and sufficient resources to make them happen. Third, the targets need to be precise, traceable and coordinated.Fourth, formally protecting a proportion of the planet’s most pristine ecosystems will by itself fall far short. Nature must be mainstreamed, incorporated in decisions made for the landscapes in which we live and work every day, well beyond protected areas. Finally, and most crucially, targets must focus on the root causes of biodiversity loss: the ways in which we consume, trade and allocate subsidies, incentives and safeguards.From previous experience, I expected objections to certain sections— pesticides and subsidies, say — but they are everywhere. Only 2 of the 22 targets have no brackets. Ironing out objections takes precious time. Because the framework can be enshrined only by consensus, too many objections can lead to too much compromise.Now, to avert failure, we exhort the governments gathering in Montreal to be brave, long-sighted and open-hearted, and to produce a visionary, ambitious biodiversity framework, grounded in knowledge. The awareness and mobilization of their constituencies has never been greater, the evidence in their hands never clearer. If not now, when?

    Competing Interests
    The author declares no competing interests. More

  • in

    Habitat types and megabenthos composition from three sponge-dominated high-Arctic seamounts

    Pitcher, T. J. et al. Seamounts: Ecology, Fisheries & Conservation (Blackwell Publishing, 2007).Book 

    Google Scholar 
    Harris, P. T., Macmillan-Lawler, M., Rupp, J. & Baker, E. K. Geomorphology of the oceans. Mar. Geol. 352, 4–24 (2014).Article 

    Google Scholar 
    Wessel, P., Sandwell, D. T. & Kim, S.-S. The global seamount census. Oceanography 23, 24–33 (2010).Article 

    Google Scholar 
    Etnoyer, P. J. et al. BOX 12|How large is the seamount biome?. Oceanography 23, 206–209 (2010).Article 

    Google Scholar 
    De Forges, B. R., Koslow, J. A. & Pooro, G. C. B. Diversity and endemism of the benthic seamount fauna in the southwest Pacific. Nature 405, 944–947 (2000).Article 
    PubMed 

    Google Scholar 
    Rowden, A. A., Dower, J. F., Schlacher, T. A., Consalvey, M. & Clark, M. R. Paradigms in seamount ecology: Fact, fiction and future. Mar. Ecol. 31, 226–241 (2010).Article 

    Google Scholar 
    Pinheiro, H. T. et al. Fish biodiversity of the Vitória-Trindade seamount chain, southwestern Atlantic: An updated database. PLoS ONE 10, 1–17 (2015).Article 

    Google Scholar 
    Morato, T., Hoyle, S. D., Allain, V. & Nicol, S. J. Seamounts are hotspots of pelagic biodiversity in the open ocean. PNAS 107, 9711 (2010).Article 

    Google Scholar 
    Rowden, A. A. et al. A test of the seamount oasis hypothesis: Seamounts support higher epibenthic megafaunal biomass than adjacent slopes. Mar. Ecol. 31, 95–106 (2010).Article 

    Google Scholar 
    Busch, K. et al. On giant shoulders: How a seamount affects the microbial community composition of seawater and sponges. Biogeosciences 17, 3471–3486 (2020).Article 
    CAS 

    Google Scholar 
    Zhao, Y. et al. Virioplankton distribution in the tropical western Pacific Ocean in the vicinity of a seamount. Microbiol Open 9, e1031 (2020).Article 

    Google Scholar 
    Arístegui, J. et al. Plankton metabolic balance at two North Atlantic seamounts. Deep-Sea Res. II 56, 2646–2655 (2009).Article 

    Google Scholar 
    Dower, J. F. & Mackast, D. L. “Seamount effects” in the zooplankton community near Cobb Seamount. Deep-Sea Res. I 43, 837–858 (1996).Article 

    Google Scholar 
    O’Hara, T. D., Rowden, A. A. & Bax, N. J. A Southern Hemisphere bathyal fauna is distributed in latitudinal bands. Curr. Biol. 21, 226–230 (2011).Article 
    PubMed 

    Google Scholar 
    Williams, A., Althaus, F., Clark, M. R. & Gowlett-Holmes, K. Composition and distribution of deep-sea benthic invertebrate megafauna on the Lord Howe Rise and Norfolk Ridge, southwest Pacific Ocean. Deep-Sea Res. II 58, 948–958 (2011).Article 
    CAS 

    Google Scholar 
    Bridges, A. E. H., Barnes, D. K. A., Bell, J. B., Ross, R. E. & Howell, K. L. Benthic assemblage composition of South Atlantic seamounts. Front. Mar. Sci. 8, 660648 (2021).Article 

    Google Scholar 
    Lapointe, A. E., Watling, L., France, S. C. & Auster, P. J. Megabenthic assemblages in the lower bathyal (700–3000 m) on the New England and corner rise seamounts Northwest Atlantic. Deep-Sea Res. I 165, 103366 (2020).Article 

    Google Scholar 
    Clark, M. R. & Bowden, D. A. Seamount biodiversity: High variability both within and between seamounts in the Ross Sea region of Antarctica. Hydrobiologia 761, 161–180 (2015).Article 
    CAS 

    Google Scholar 
    McClain, C. R., Lundsten, L., Barry, J. & DeVogelaere, A. Assemblage structure, but not diversity or density, change with depth on a northeast Pacific seamount. Mar. Ecol. 31, 14–25 (2010).Article 

    Google Scholar 
    Long, D. J. & Baco, A. R. Rapid change with depth in megabenthic structure-forming communities of the Makapu’u deep-sea coral bed. Deep-Sea Res. II 99, 158–168 (2014).Article 

    Google Scholar 
    Thresher, R. et al. Strong septh-related zonation of megabenthos on a rocky continental margin (∼ 700–4000 m) off southern Tasmania Australia. PLoS ONE 9, e85872 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    O’Hara, T. D., Consalvey, M., Lavrado, H. P. & Stocks, K. I. Environmental predictors and turnover of biota along a seamount chain. Mar. Ecol. 31, 84–94 (2010).Article 

    Google Scholar 
    Boschen, R. E. et al. Megabenthic assemblage structure on three New Zealand seamounts: Implications for seafloor massive sulfide mining. Mar. Ecol. Prog. Ser. 523, 1–14 (2015).Article 

    Google Scholar 
    Caratori Tontini, F. et al. Crustal magnetization of brothers volcano, New Zealand, measured by autonomous underwater vehicles: Geophysical expression of a submarine hydrothermal system. Econ. Geol. 107, 1571–1581 (2012).Article 

    Google Scholar 
    Rex, M. A., Etter, R. J., Clain, A. J. & Hill, M. S. Bathymetric patterns of body size in deep-sea gastropods. Evolution (N Y) 53, 1298–1301 (1999).
    Google Scholar 
    O’Hara, T. D. Seamounts: Centres of endemism or species richness for ophiuroids?. Glob. Ecol. Biogeogr. 16, 720–732 (2007).Article 

    Google Scholar 
    Clark, M. R. et al. The ecology of seamounts: Structure, function, and human impacts. Ann. Rev. Mar. Sci. 2, 253–278 (2010).Article 
    PubMed 

    Google Scholar 
    Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1, 443–466 (2009).Article 
    PubMed 

    Google Scholar 
    Levin, L. A. & Thomas, C. L. The influence of hydrodynamic regime on infaunal assemblages inhabiting carbonate sediments on central Pacific seamounts. Deep Sea Res. A 36, 1897–1915 (1989).Article 

    Google Scholar 
    Puerta, P. et al. Variability of deep-sea megabenthic assemblages along the western pathway of the Mediterranean outflow water. Deep-Sea Res. I 185, 103791 (2022).Article 

    Google Scholar 
    Tapia-Guerra, J. M. et al. First description of deep benthic habitats and communities of oceanic islands and seamounts of the Nazca Desventuradas Marine Park Chile. Sci. Rep. 11, 6209 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morgan, N. B., Goode, S., Roark, E. B. & Baco, A. R. Fine scale assemblage structure of benthic invertebrate megafauna on the North Pacific seamount Mokumanamana. Front. Mar. Sci. 6, 715 (2019).Article 

    Google Scholar 
    Perez, J. A. A., Kitazato, H., Sumida, P. Y. G., Sant’Ana, R. & Mastella, A. M. Benthopelagic megafauna assemblages of the Rio Grande Rise (SW Atlantic). Deep-Sea Res. I 134, 1–11 (2018).Article 

    Google Scholar 
    Poore, G. C. B. et al. Invertebrate diversity of the unexplored marine western margin of Australia: Taxonomy and implications for global biodiversity. Mar. Biodivers. 45, 271–286 (2015).Article 

    Google Scholar 
    Henry, L. A., Moreno Navas, J. & Roberts, J. M. Multi-scale interactions between local hydrography, seabed topography, and community assembly on cold-water coral reefs. Biogeosciences 10, 2737–2746 (2013).Article 

    Google Scholar 
    Meyer, K. S. et al. Rocky islands in a sea of mud: Biotic and abiotic factors structuring deep-sea dropstone communities. Mar. Ecol. Prog. Ser. 556, 45–57 (2016).Article 

    Google Scholar 
    Stratmann, T., Soetaert, K., Kersken, D. & van Oevelen, D. Polymetallic nodules are essential for food-web integrity of a prospective deep-seabed mining area in Pacific abyssal plains. Sci. Rep. 11, 12238 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Genin, A., Dayton, P. K., Lonsdale, P. F. & Spiess, F. N. Corals on seamount peaks provide evidence of current acceleration over deep-sea topography. Nature 322, 59–61 (1986).Article 

    Google Scholar 
    Roberts, J. M., Wheeler, A. J. & Freiwald, A. Reefs of the deep: The biology and geology of cold-water coral ecosystems. Science 1979(312), 543–547 (2006).Article 

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

    Google Scholar 
    Beazley, L., Kenchington, E. L., Murillo, F. J. & Sacau, M. D. M. Deep-sea sponge grounds enhance diversity and abundance of epibenthic megafauna in the Northwest Atlantic. ICES J. Mar. Sci. 70, 1471–1490 (2013).Article 

    Google Scholar 
    Buhl-Mortensen, L. et al. Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins. Mar. Ecol. 31, 21–50 (2010).Article 

    Google Scholar 
    Victorero, L., Robert, K., Robinson, L. F., Taylor, M. L. & Huvenne, V. A. I. Species replacement dominates megabenthos beta diversity in a remote seamount setting. Sci. Rep. 8, 1–11 (2018).Article 
    CAS 

    Google Scholar 
    Yesson, C., Clark, M. R., Taylor, M. L. & Rogers, A. D. The global distribution of seamounts based on 30 arc seconds bathymetry data. Deep-Sea Res. I 58, 442–453 (2011).Article 

    Google Scholar 
    ICES. Report of the ICES-NAFO Working Group on Deep-Water Ecology (WGDEC), 9–13 March 2009, ICES CM2009ACOM:23. 2009.Cárdenas, P. & Rapp, H. T. Demosponges from the Northern mid-Atlantic ridge shed more light on the diversity and biogeography of North Atlantic deep-sea sponges. J. Mar. Biol. Assoc. U.K. 95, 1475–1516 (2015).Article 

    Google Scholar 
    Cárdenas, P. et al. Taxonomy, biogeography and DNA barcodes of Geodia species (Porifera, Demospongiae, Tetractinellida) in the Atlantic boreo-arctic region. Zool. J. Linn. Soc. 169, 251–311 (2013).Article 

    Google Scholar 
    Roberts, E. M. et al. Oceanographic setting and short-timescale environmental variability at an Arctic seamount sponge ground. Deep-Sea Res. I 138, 98–113 (2018).Article 

    Google Scholar 
    Roberts, E. et al. Water masses constrain the distribution of deep-sea sponges in the North Atlantic Ocean and Nordic seas. Mar. Ecol. Prog. Ser. 659, 75–96 (2021).Article 

    Google Scholar 
    Morganti, T. M. et al. Giant sponge grounds of central Arctic seamounts are associated with extinct seep life. Nat. Commun. 13, 638 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morganti, T. M. et al. In situ observation of sponge trails suggests common sponge locomotion in the deep central Arctic. Curr. Biol. 31, R368–R370 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep-Sea Res. I 153, 103137 (2019).Article 

    Google Scholar 
    McIntyre, F. D., Drewery, J., Eerkes-Medrano, D. & Neat, F. C. Distribution and diversity of deep-sea sponge grounds on the Rosemary bank seamount NE Atlantic. Mar. Biol. 163, 143 (2016).Article 

    Google Scholar 
    Buhl-Mortensen, P. & Buhl-Mortensen, L. Diverse and vulnerable deep-water biotopes in the Hardangerfjord. Mar. Biol. Res. 10, 253–267 (2014).Article 

    Google Scholar 
    de Clippele, L. H. et al. The effect of local hydrodynamics on the spatial extent and morphology of cold-water coral habitats at Tisler Reef Norway. Coral Reefs 37, 253–266 (2018).Article 
    PubMed 

    Google Scholar 
    Dunlop, K., Harendza, A., Plassen, L. & Keeley, N. Epifaunal habitat Associations on mixed and hard bottom substrates in coastal waters of Northern Norway. Front. Mar. Sci. 7, 568802 (2020).Article 

    Google Scholar 
    Fiore, C. L. & Cox Jutte, P. Characterization of macrofaunal assemblages associated with sponges and tunicates collected off the southeastern United States. Biology 129, 105–120 (2010).
    Google Scholar 
    Murillo, F. J. et al. Deep-sea sponge grounds of the Flemish Cap, Flemish Pass and the Grand Banks of Newfoundland (Northwest Atlantic Ocean): Distribution and species composition. Mar. Biol. Res. 8, 842–854 (2012).Article 

    Google Scholar 
    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 (2013).Article 

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

    Google Scholar 
    Klitgaard, A. B. The fauna associated with outer shelf and upper slope sponges (porifera, demospongiae) at the faroe islands, northeastern Atlantic. Sarsia 80, 1–22 (1995).Article 

    Google Scholar 
    Cárdenas, P. & Moore, J. A. First records of Geodia demosponges from the New England seamounts, an opportunity to test the use of DNA mini-barcodes on museum specimens. Mar. Biodivers. 49, 163–174 (2019).Article 

    Google Scholar 
    Schejter, L., Chiesa, I. L., Doti, B. L. & Bremec, C. Mycale (Aegogropila) magellanica (Porifera: Demospongiae) in the southwestern Atlantic Ocean: Endobiotic fauna and new distributional information. Sci. Mar. 76, 753–761 (2012).
    Google Scholar 
    Beaulieu, S. E. Life on glass houses: Sponge stalk communities in the deep sea. Mar. Biol. 138, 803–817 (2001).Article 

    Google Scholar 
    Goren, L., Idan, T., Shefer, S. & Ilan, M. Macrofauna inhabiting massive demosponges from shallow and mesophotic habitats along the Israeli Mediterranean coast. Front. Mar. Sci. 7, 612779 (2021).Article 

    Google Scholar 
    Kersken, D. et al. The infauna of three widely distributed sponge species (Hexactinellida and Demospongiae) from the deep Ekström Shelf in the Weddell Sea Antarctica. Deep-Sea Res. II 108, 101–112 (2014).Article 

    Google Scholar 
    Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep Sea Res. 1 Oceanogr. Res. Pap. 153, 103137 (2019).Article 

    Google Scholar 
    Bart, M. C., Hudspith, M., Rapp, H. T., Verdonschot, P. F. M. & de Goeij, J. M. A Deep-Sea Sponge Loop? Sponges transfer dissolved and particulate organic carbon and nitrogen to associated fauna. Front. Mar. Sci. 8, 604879 (2021).Article 

    Google Scholar 
    de Goeij, J. M. et al. Surviving in a marine desert: The sponge loop retains resources within coral reefs. Science 1979(342), 108–110 (2013).Article 

    Google Scholar 
    Pawlik, J. R. & Mcmurray, S. E. The emerging ecological and biogeochemical importance of sponges on coral reefs. (2019) https://doi.org/10.1146/annurev-marine-010419Wassmann, P., Slagstad, D. & Ellingsen, I. Primary production and climatic variability in the European sector of the Arctic Ocean prior to 2007: Preliminary results. Polar Biol. 33, 1641–1650 (2010).Article 

    Google Scholar 
    Arrigo, K. R., van Dijken, G. & Pabi, S. Impact of a shrinking Arctic ice cover on marine primary production. Geophys. Res. Lett. 35, L19603 (2008).Article 

    Google Scholar 
    Dunne, J. P., Sarmiento, J. L. & Gnanadesikan, A. A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor. Glob. Biogeochem. Cycles 21, GB4006 (2007).Article 

    Google Scholar 
    Wei, C.-L. et al. Global patterns and predictions of seafloor biomass using random forests. PLoS ONE 5, e15323 (2010).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stratmann, T. et al. The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities. Sci. Data 7, 206 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McClain, C. R., Lundsten, L., Ream, M., Barry, J. & DeVogelaere, A. Endemicity, biogeography, composition, and community structure on a Northeast Pacific seamount. PLoS ONE 4, e4141 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Walter, M., Köhler, J., Myriel, H., Steinmacher, B. & Wisotzki, A. Physical oceanography measured on water bottle samples during POLARSTERN cruise PS101 (ARK-XXX/3). PANGAEA https://doi.org/10.1594/PANGAEA.871927 (2017).van Appen, W.-J., Latarius, K. & Kanzow, T. Physical oceanography and current meter data from mooring F6–17. PANGAEA https://doi.org/10.1594/PANGAEA.870845 (2017).Ruhl, H. A. & Smith, K. L. Shifts in deep-sea community structure linked to climate and food supply. Science 1979(305), 513–515 (2004).Article 

    Google Scholar 
    Boetius, A. et al. Export of algal biomass from the melting Arctic sea ice. Science 1979(339), 1430–1432 (2013).Article 

    Google Scholar 
    Rybakova, E., Kremenetskaia, A., Vedenin, A., Boetius, A. & Gebruk, A. Deep-sea megabenthos communities of the Eurasian Central Arctic are influenced by ice-cover and sea-ice algal falls. PLoS ONE 14, e0211009 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhulay, I., Bluhm, B. A., Renaud, P. E., Degen, R. & Iken, K. Functional pattern of benthic epifauna in the Chukchi borderland Arctic deep sea. Front. Mar. Sci. 8, 609956 (2021).Article 

    Google Scholar 
    Boetius, A. & Purser, A. The expedition PS101 of the research vessel Polarstern to the Arctic Ocean in 2016. Berichte zur Polar-und Meeresforschung = Rep Polar Mar Res https://doi.org/10.2312/BzPM_0706_2017 (2017).Article 

    Google Scholar 
    Simon-Lledó, E. et al. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone. Prog. Oceanogr. 187, 102405 (2020).Article 

    Google Scholar 
    Simon-Lledó, E. et al. Preliminary observations of the abyssal megafauna of Kiribati. Front. Mar. Sci. 6, 1–13 (2019).Article 

    Google Scholar 
    Zhulay, I., Iken, K., Renaud, P. E. & Bluhm, B. A. Epifaunal communities across marine landscapes of the deep Chukchi Borderland (Pacific Arctic). Deep Sea Res. 1 Oceanogr. Res. Pap. 151, 103065 (2019).Article 

    Google Scholar 
    Åström, E. K. L., Sen, A., Carroll, M. L. & Carroll, J. L. Cold seeps in a warming Arctic: Insights for benthic ecology. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00244 (2020).Article 

    Google Scholar 
    Pedersen, R. B. et al. Discovery of a black smoker vent field and vent fauna at the Arctic Mid-Ocean Ridge. Nat. Commun. 1, 1–6 (2010).Article 
    CAS 

    Google Scholar 
    Åström, E. K. L. et al. Methane cold seeps as biological oases in the high-Arctic deep sea. Limnol. Oceanogr. 63, S209–S231 (2018).Article 

    Google Scholar 
    Rybakova Goroslavskaya, E., Galkin, S., Bergmann, M., Soltwedel, T. & Gebruk, A. Density and distribution of megafauna at the Håkon Mosby mud volcano (the Barents Sea) based on image analysis. Biogeosciences 10, 3359–3374 (2013).Article 

    Google Scholar 
    Sweetman, A. K., Levin, L. A., Rapp, H. T. & Schander, C. Faunal trophic structure at hydrothermal vents on the southern mohn’s ridge, arctic ocean. Mar. Ecol. Prog. Ser. 473, 115–131 (2013).Article 

    Google Scholar 
    Decker, C. & Olu, K. Does macrofaunal nutrition vary among habitats at the Hakon Mosby mud volcano?. Cah. Biol. Mar. 51, 361–367 (2010).
    Google Scholar 
    Macdonald, I. R., Bluhm, B. A., Iken, K., Gagaev, S. & Strong, S. Benthic macrofauna and megafauna assemblages in the Arctic deep-sea Canada Basin. Deep-Sea Res. II 57, 136–152 (2010).Article 

    Google Scholar 
    Taylor, J., Krumpen, T., Soltwedel, T., Gutt, J. & Bergmann, M. Dynamic benthic megafaunal communities: Assessing temporal variations in structure, composition and diversity at the Arctic deep-sea observatory HAUSGARTEN between 2004 and 2015. Deep Sea Res. 1 Oceanogr. Res. Pap. 122, 81–94 (2017).Article 

    Google Scholar 
    Vedenin, A. A. et al. Uniform bathymetric zonation of marine benthos on a Pan-Arctic scale. Prog. Oceanogr. 202, 102764 (2022).Article 

    Google Scholar 
    Bart, M. C. et al. A deep-sea sponge loop? Sponges transfer dissolved and particulate organic carbon and nitrogen to associated fauna. Front. Mar. Sci. 8, 604879 (2021).Article 

    Google Scholar 
    Guihen, D., White, M. & Lundälv, T. Temperature shocks and ecological implications at a cold-water coral reef. ANZIAM J. https://doi.org/10.1017/S1755267212000413 (2014).Article 

    Google Scholar 
    Strand, R. et al. The response of a boreal deep-sea sponge holobiont to acute thermal stress. Sci. Rep. 7, 1660 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hanz, U. et al. The important role of sponges in carbon and nitrogen cycling in a deep-sea biological hotspot. Funct. Ecol. 36, 2188–2199 (2022).Article 
    CAS 

    Google Scholar 
    Maier, S. R. et al. Reef communities associated with ‘dead’ cold-water coral framework drive resource retention and recycling in the deep sea. Deep-Sea Res. I 175, 103574 (2021).Article 
    CAS 

    Google Scholar 
    Bart, M. C. et al. Dissolved organic carbon (DOC) is essential to balance the metabolic demands of four dominant North-Atlantic deep-sea sponges. Limnol. Oceanogr. https://doi.org/10.1002/lno.11652 (2020).Article 

    Google Scholar 
    Bart, M. C. et al. Differential processing of dissolved and particulate organic matter by deep-sea sponges and their microbial symbionts. Sci. Rep. 10, 1–13 (2020).Article 

    Google Scholar 
    Maier, S. R. et al. Recycling pathways in cold-water coral reefs: Use of dissolved organic matter and bacteria by key suspension feeding taxa. Sci. Rep. 10, 9942 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    International Hydrographic Bureau. 16th meeting of the GEBCO sub-committee on undersea feature names (SCUFN). Preprint at (2003).Torres-Valdés, S., Morische, A. & Wischnewski, L. Revision of nutrient data from Polarstern expedition PS101 (ARK-XXX/3). PANGAEA https://doi.org/10.1594/PANGAEA.908179 (2019).Purser, A. et al. Ocean floor observation and bathymetry system (OFOBS): A new towed camera/sonar system for deep-sea habitat surveys. IEEE J. Ocean. Eng. 44, 87–99 (2019).Article 

    Google Scholar 
    Marcon, Y. & Purser, A. PAPARA(ZZ)I : An open-source software interface for annotating photographs of the deep-sea. SoftwareX 6, 69–80 (2017).Article 

    Google Scholar 
    Greene, H. G., Bizzarro, J. J., O’Connell, V. M. & Brylinsky, C. K. Construction of digital potential marine benthic habitat maps using a coded classification scheme and its application. Spec. Pap.: Geol. Assoc. Canada 47, 141–155 (2007).
    Google Scholar 
    Horton, T. et al. Recommendations for the standardisation of open taxonomic nomenclature for image-based identifications. Front. Mar. Sci. 8, 620702 (2021).Article 

    Google Scholar 
    Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Application (Cambridge University Press, 1997).Book 
    MATH 

    Google Scholar 
    Rodgers, J. L. The bootstrap, the jackknife, and the randomization test: A sampling taxonomy. Multivar. Behav. Res. 34, 441–456 (1999).Article 
    CAS 

    Google Scholar 
    Crowley, P. H. Resampling methods for computation-intensive data analysis in ecology and evolution. Annu. Rev. Ecol. Syst. 23, 405–447 (1992).Article 

    Google Scholar 
    Simon-Lledó, E. et al. Ecology of a polymetallic nodule occurrence gradient: Implications for deep-sea mining. Limnol. Oceanogr. 64, 1883–1894 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

    Google Scholar 
    Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

    Google Scholar 
    R-Core Team. R: A language and environment for statistical computing. Preprint at https://www.r-project.org/ (2017).Oksanen, J. et al. vegan: Community ecology package. Preprint at (2017).Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260 (2013).Article 

    Google Scholar 
    Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. 69, 1–17 (2016).Article 

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

    Google Scholar 
    de Kluijver, A. Fatty acid analysis sponges. protocols.io 1, 1–14. https://doi.org/10.17504/protocols.io.bhnpj5dn (2021).Article 

    Google Scholar 
    de Kluijver, A. et al. Bacterial precursors and unsaturated long-chain fatty acids are biomarkers of North-Atlantic deep-sea demosponges. PLoS ONE 16, e0241095 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More