More stories

  • in

    The Late Miocene Rifian corridor as a natural laboratory to explore a case of ichnofacies distribution in ancient gateways

    Oceanic gateways play a key role in controlling global ocean circulation and climate systems1. Ancient seaways are unique environments in which a complex interplay of processes may take place (i.e., oceanic-, tidal-, bottom-, turbiditic- and wind-currents)2,3. The constricted morphology of the seaway usually funnels and amplifies the currents that shape the seafloor (i.e., tidal currents)4. Previous sedimentological studies of ancient seaways have been largely focussed on shallow counterparts (generally between 100 and 150 m of water depth)4,5,6. Few published examples of deep ancient seaways ( > 150 m) and associated deposits can be found. However, oceanographic studies have shown that deep seaways are different from shallow ones, with bottom-currents sometimes playing a dominant role7,8,9. The Rifian Corridor is one of those few examples (Fig. 1)2,3,10,11.
    Figure 1

    Palaeogeographic reconstruction of the late Miocene western Mediterranean with the location of the studied outcrops; red (lower) and orange (upper) arrows show palaeo-Mediterranean Outflow Water (palaeo-MOW) branches (modified from de Weger et al.2). Below, schematic sedimentary logs of the studied outcrops. Map created with Adobe Illustrator, version 22.1.0 (https://www.adobe.com/products/illustrator.html).

    Full size image

    During the late Miocene, the Atlantic Ocean and the Mediterranean Sea were connected by two principal gateways, with a complex morphology, sills and channels through south Iberia and north Africa —the Betic and Rifian corridors, respectively12,13. The Rifian Corridor was a main deep seaway of this network (Fig. 1). This gateway progressively closed (7.1–6.9 Ma) due to tectonically induced uplift, leading to the onset of the Mediterranean Salinity Crisis in the late Miocene13,14. During the late Tortonian, the seaway evolved into a narrow, deep corridor hosting a complex interplay of processes2,3.
    Ichnological analysis comprises a wide range of tools (e.g., ichnofabric approach, ichnofacies model) that prove very useful in sedimentary basin research15. The ichnofacies model is of special interest for detailed palaeoenvironmental reconstructions and for recognizing, distinguishing, and interpreting sedimentary environments16,17,18,19. Recent steps in ichnological research have established means of recognising and characterising contouritic processes, revealing the importance of ichnology as a proxy for discerning between contourites, turbidites, hemipelagites and pelagites20,21,22,23,24, but not without scepticism25. At any rate, the relationship between deep-sea settings and trace fossils is very complex, and depends highly upon the palaeonvironmental factors that affect trace makers26.
    Trace-fossil research on seaway environments has been conducted mainly on shallow marine settings, including brackish-water ecosystems (i.e., estuarine complexes, resulting in the so-called “brackish-water model”27,28), beach–shoreface complexes with evidence of tidal processes29,30, and compound dune fields31. Still, detailed trace-fossil analysis and ichnofacies characterisation of ancient deep seaways has never been carried out. The aim of this research is to conduct a detailed ichnological analysis of selected outcrops of the Rifian Corridor (Ain Kansera, Sidi Chahed, Kirmta and Sidi Harazem), as a unique opportunity to assess trace-fossil variations to interpret an ancient deep-water seaway where shallow marine processes (i.e., tidal variations), pelagic/hemipelagic settling, turbiditic supplies and contouritic flows closely (less than 20 km) interact2,3. We evaluate the importance of palaeoenvironmental factors such as nutrients, oxygenation, and flow velocity in a setting dominated by bottom currents, and their incidence on the trace maker community. The utility of the ichnofacies approach is underlined within the framework of improving high-resolution palaeoenvironmental reconstructions in different depositional environments of ancient deep gateways.
    Trace-fossil assemblages at the Rifian Corridor
    In both contouritic and turbiditic deposits, ichnodiversity is low (4 and 5 ichnogenera, respectively), whereas trace-fossil abundance is high in the former and moderate in the latter. Shallow marine deposits from the southern Rifian Corridor feature an abundant and moderately diverse trace-fossil assemblage (9 ichnogenera). Within the selected outcrops, the clear ichnological variability can be attributed to the different facies.
    The Sidi Harazem turbiditic ichnoassemblage consists of 5 ichnogenera —Ophiomorpha (O. rudis), Planolites, Spirophyton, Thalassinoides, and Zoophycos (Fig. 3E–H)— and the thick sandstone beds are more bioturbated than the marly ones. Ophiomorpha is the most abundant ichnogenus, and appears in the thick turbiditic sandstone beds; Thalassinoides is common, Planolites rare, and Zoophycos and Spirophyton is occasionally found. The trace-fossil assemblage of marly pelagic and hemipelagic deposits from the Sidi Harazem consists of abundant undifferentiated structures and scarce Planolites-like and Thalassinoides-like trace fossils.
    The sandy contourites in Kirmta and Sidi Chahed comprise a highly abundant and scarcely diverse trace-fossil assemblage (4 ichnogenera), dominated by Macaronichnus and Scolicia, and common Planolites and Thalassinoides (Fig. 2). Trace fossils were predominantly found in the planar-stratified and cross-bedded sandstone. Turbidites show an absence of discrete trace fossils. The trace = fossil assemblage of muddy contourite deposits from both outcrops consist of regular undifferentiated biogenic structures and scarce Planolites-like and Thalassinoides-like trace fossils.
    Figure 2

    Trace-fossil specimens from the sandy contourite deposits at Sidi Chahed (A–D) and Kirmta (E–H) outcrops. (A, B) Scolicia in the sole of sandy clastic contouritic beds of Sidi Chahed; (C) Close-up view of Macaronichnus at Sidi Chahed; (D) Planolites within the interbedding of the foresets at Sidi Chahed. (E) Scolicia and some Macaronichnus at Kirmta; (F, G) Macaronichnus isp. and some Thalassinoides in the sole of sandy clastic contouritic beds at Kirmta; (H) Close-up view of Macaronichnus at Kirmta. Macaronichnus (Ma), Planolites (Pl), Scolicia (Sc), and Thalassinoides (Th).

    Full size image

    The Ain Kansera section is characterised by a shallow marine ichnoassemblage with high ichnodiversity and an abundance of vertical structures, including 9 ichnogenera in the sandstone beds: Conichnus, Diplocraterion, Macaronichnus, Ophiomorpha, Parahaentzschelinia, Planolites, Scolicia, Skolithos, and Thalassinoides (Fig. 3A–D). The sandstone beds with swaley cross-stratification show a change in the trace-fossil assemblage towards the top of the outcrop. The lower sandstone beds present dominant Conichnus and Macaronichnus, common Parahaentzschelinia and Thalassinoides, and rare Diplocraterion, Planolites, and Scolicia. The upper sandstone beds record the disappearance of Conichnus and Parahaentzschelinia, while Ophiomorpha and Skolithos become dominant.
    Figure 3

    Trace-fossil specimens from shallow marine deposits at Ain Kansera (A–D) and turbiditic deposits at Sidi Harazem (E–H). (A) Close-up view of Macaronichnus at Ain Kansera; (B) Densely Conichnus assemblage at Ain Kansera; (C) Macaronichnus cross-cut by a Skolithos at Ain Kansera; (D) Skolithos and Ophiomorpha at Ain Kansera; (E, F) Ophiomorpha (O. rudis) at Sidi Harazem; (G) Zoophycos cross-cut by a Thalassinoides at Sidi Harazem; (H) Close-up view of Spyrophyton at Sidi Harazem. Conichnus (Co), Macaronichnus (Ma), Ophiomorpha (Op), Skolithos (Sk), Spyrophyton (Sp), Thalassinoides (Th), and Zoophycos (Zo).

    Full size image

    Ichnofacies characterisation
    The trace-fossil assemblage of Sidi Harazem is typified by vertical burrows of Ophiomorpha rudis and some Thalassinoides. Ophiomorpha is generally but not exclusively characteristic of high-energy environments (i.e., shoreface) in well-sorted, shifting sandy substrates, constituting a common element of the Skolithos and Cruziana ichnofacies17,18. However, the appearance of Ophiomorpha in deep-sea environments is also recorded, and usually explained as an effect of transport of the trace makers by currents from shallow marine environments into the deep-sea33,34. Uchman35 proposed the Ophiomorpha rudis ichnosubfacies within the Nereites ichnofacies for the record of ichnoassemblages dominated by Ophiomorpha rudis in thick sandstone beds related with channels and proximal lobes in turbiditic systems36. Accordingly, the Sidi Harazem trace-fossil assemblage could be associated with the Ophiomorpha rudis ichnosubfacies. Ichnosubfacies/ichnofacies assignation is tentative due to the absence of other components of this ichnosubfacies (e.g., Scolicia, Nereites, graphoglyptids); this uncertainty is tied to outcrop limitations, e.g. the low exposure of turbiditic soles and difficulties in observing discrete trace fossils in the non-compact hemipelagic and pelagic deposits.
    The trace-fossil assemblages of Kirmta and Sidi Chahed feature high abundance and low ichnodiversity, being dominated by horizontal trace fossils, such as Macaronichnus and Scolicia. Macaronichnus is usually interpreted as a shallow marine (up to foreshore) trace fossil37 that occasionally appears in deeper water environments38,39 and is commonly associated with the Skolithos ichnofacies17,18,19,40. Scolicia presents a wide environmental range, but is a typical element of the deep-marine Nereites and the shelfal Cruziana ichnofacies40. The proximal expression of the Cruziana ichnofacies is dominated by deposit-feeding burrows, but also includes structures of passive carnivores, omnivores, suspension feeders, as well as grazing forms41. This ichnofacies is defined as a transition between the distal expression of the Skolithos ichnofacies and the archetypal Cruziana ichnofacies41. The low ichnodiversity observed within the contourite facies from Kirmta and Sidi Chahed outcrops, together with the ubiquity of the dominant trace fossils, hamper a conclusive ichnofacies assignation. Still, though Macaronichnus is typical from high energy shallow marine environments, it may locally appear in the proximal Cruziana ichnofacies41. Considering the dominance of horizontal feeding trace fossils produced by deposit and detritus feeders over dwelling structures of suspension feeding structures, contourite ichnoassemblages at the Rifian Corridor, registered at Kirmta and Sidi Chahed outcrops, can therefore be tentatively assigned to an impoverished proximal Cruziana ichnofacies18.
    The trace-fossil assemblage of Ain Kansera is characterised by moderate ichnodiversity with a dominance of vertical (Skolithos and Ophiomorpha), cylindrical or conic-shaped (Conichnus) dwelling burrows of suspension feeders and passive predators. Horizontal trace fossils produced by a mobile fauna are scarce, mainly associated with Macaronichnus trace makers. According to these ichnological features, shallow marine facies at the Rifian Corridor —represented by Ain Kansera sediments— can be clearly assigned to the Skolithos ichnofacies, with predominant burrow systems having vertical, cylindrical, or U-shaped components of suspension feeders and passive predators, and a scarcity of horizontal trace fossils17,18,19,40,42.
    Ichnofacies in the Rifian Corridor seaways: hydrodynamic energy and the incidence of bottom currents
    Over the past years, detailed ichnological research has revealed the major incidence of particular environmental factors (e.g., organic-matter content, oxygenation, sedimentation rate) on ichnological attributes from deep-sea environments, including ichnofacies characterisation and distribution26. The deep sea is a complex environment where several depositional processes co-exist, including pelagic/hemipelagic settling, bottom currents and gravity flows9. Trace-fossil analysis has proven useful for discerning and characterising such sedimentary environments and associated deposits21. Hydrodynamic conditions are a very significant limiting factor for trace makers, inducing variations in distribution and behaviour, hence in the preservation of trace fossils19,29,43,44. Typically, ichnoassemblages related to high energy conditions are characterised by vertical dwelling structures of infaunal suspension feeders and/or passive predators, forming low-diversity suites; ichnoassemblages related to low energy conditions are dominated by horizontal feeding trace fossils of deposit and detritus feeders, as well as higher diversity19. Ichnofacies identification is mainly based on the recognition of key features that connect biological structures with physical parameters (i.e., environmental conditions)17,18,19. Accordingly, ichnofacies reflect specific combinations of organisms´ responses to a wide range of environmental conditions.
    In the case of seaways, prevailing hydrodynamic conditions are a main environmental factor, along with controlling depositional processes and sedimentation regimes6,30. Even though the number of trace-fossil studies is considerably lower than in other clastic shallow or deep marine environments, ichnological analysis has proven to be useful to characterise waves, tides or storms in shallow seaways29,30, overlooking deep seaways and their implications. Deep seaways with narrow palaeogeographical configuration, as is the case of the Rifian Corridor10, would promote higher energetic conditions than those typical of deep-sea environments. In the study area, clearly distinct sedimentary environments —in terms of hydrodynamic conditions, bathymetry, rate of sedimentation, etc.— are closely spaced2, passing from shallow marine to turbiditic slope systems in less than 20 km (Fig. 4). Such variations in palaeoenvironmental conditions are supported by ichnofacies characterisation and distribution.
    Figure 4

    Palaeogeographic model of the late Miocene Rifian Corridor (Morocco) with ichnofacies distribution (lower red and upper orange branches indicate palaeo-MOW location; modified from de Weger et al.2). Conichnus (Co), Diplocraterion (Di), Macaronichnus (Ma), Ophiomorpha (Op), Parahaentzschelinia (Ph), Planolites (Pl), Scolicia (Sc), Skolithos (Sk), Spyrophyton (Sp), Thalassinoides (Th), and Zoophycos (Zo).

    Full size image

    Turbidite deposits from Sidi Harazem, emplaced on the slope of the Rifian Corridor, are typified by vertical trace fossils, mainly by the record of Ophiomorpha rudis. These ichnological attributes are similar to those associated with particular sub-environments (e.g., channels and proximal trubiditic lobes) of the turbiditic systems, conforming the Ophiomorpha rudis ichnosubfacies inside the Nereites ichnofacies36.
    Sandy contourite 2D- and 3D-dune facies (upper slope environment) (Fig. 4) from Sidi Chahed and Kirmta are related to high-energy deep-water environments. However, they are dominated by horizontal trace fossils (Macaronichnus and Scolicia) produced by mobile deposit- and detritus-feeders, discarding a direct assignation to the Skolithos ichnofacies. In this case, palaeoenvironmental conditions other than hydrodynamic energy must be considered to explain the dominance of horizontal forms and the absence of vertical biogenic structures. The record of densely Macaronichnus ichnoasemblages in these contourite sediments was recently linked to high nutrient supply provided by ancient bottom currents39,45. This agrees with the record of Scolicia: its abundance and size usually increase in conjunction with greater amounts and nutritious values of benthic food20,46. Thus, the strong palaeo-MOW bottom currents that dominated the slope may have created well-oxygenated and nutrient-rich benthic environments, favouring colonisation by trace makers that could exploit such accumulations of organic matter inside the sediment. Macaronichnus and Scolicia producers could develop an opportunistic behaviour, determining rapid and complete bioturbation, avoiding colonisation by other trace makers —including suspension feeders—these ichnological features resemble the Cruziana ichnofacies attributes. Notwithstanding, the high ichnodiversity that is characteristic of the Cruziana ichnofacies is absent here. The great abundance and low ichnodiversity observed for the contourite facies appear to indicate the absence of an archetypal Cruziana ichnofacies, but the development of the proximal Cruziana ichnofacies. Bottom currents and their associated deposits (i.e., contourites) have been previously linked to both the Cruziana and Zoophycos ichnofacies in Cyprus Miocene carbonate contourite deposits22,23, meaning that contourite deposits are not exclusively related to a single ichnofacies. The replacement from the Zoophycos to Cruziana ichnofacies was interpreted to be mainly controlled by sea level dynamics23.
    The shallow marine facies from Ain Kansera (shoreface environment) are dominated by vertical, cylindrical, or U-shaped dwelling burrows (Conichnus, Ophiomorpha and Skolithos) of suspension feeders (Fig. 4). These attributes are usually related to high energetic conditions developed in shallow marine environments conforming the Skolithos ichnofacies18.
    In short, at the Rifian Corridor, ichnofacies distributions from proximal to distal settings are controlled by bottom currents (palaeo-MOW), with hydrodynamic conditions being the major palaeonvironmental limiting factor. Particularly noteworthy is the development of the proximal Cruziana ichnofacies in deeper settings from the slope environments; bottom currents generated high energetic conditions similar to those of shallow/proximal areas. More

  • in

    Conservation priorities in an endangered estuarine seahorse are informed by demographic history

    1.
    Ramos-Onsins, S. E. & Rozas, J. Statistical properties of new neutrality tests against population growth. Mol. Biol. Evol. 19, 2092–2100 (2000).
    Article  Google Scholar 
    2.
    Wan, Q.-H., Wu, H., Fujihara, T. & Fang, S.-G. Which genetic marker for which conservation genetics issue? Electrophoresis 25, 2165–2176 (2004).
    CAS  PubMed  Article  Google Scholar 

    3.
    Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).
    PubMed  Article  Google Scholar 

    4.
    Rogers, A. R. & Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992).
    CAS  PubMed  Google Scholar 

    5.
    Nabholz, B., Mauffrey, J.-F., Bazin, E., Galtier, N. & Glemin, S. Determination of mitochondrial genetic diversity in mammals. Genetics 178, 351–361 (2008).
    PubMed  PubMed Central  Article  Google Scholar 

    6.
    Whitfield, A., Mkare, T. K., Teske, P. R., James, N. & Cowley, P. D. Life-histories explain the conservation status of two estuary-associated pipefishes. Biol. Conserv. 212, 256–264 (2017).
    Article  Google Scholar 

    7.
    Leffler, E. M. et al. Revisiting an old riddle: what determines genetic diversity levels within species?. PLoS Biol. 10, e1001388 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    8.
    Kimura, M. & Crow, J. F. The number of alleles that can be maintained in a finite population. Genetics 49, 725–738 (1964).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    9.
    Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, 1983).
    Google Scholar 

    10.
    Romiguier, J. et al. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515, 261–263 (2014).
    ADS  CAS  PubMed  Article  Google Scholar 

    11.
    Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).
    CAS  PubMed  Article  Google Scholar 

    12.
    Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Annu. Rev. Ecol. Syst. 27, 477–500 (1996).
    Article  Google Scholar 

    13.
    Teske, P. R. et al. Implications of life history for genetic structure and migration rates of southern African coastal invertebrates: planktonic, abbreviated and direct development. Mar. Biol. 152, 697–711 (2007).
    Article  Google Scholar 

    14.
    Mkare, T. K., van Vuuren, B. J. & Teske, P. R. Conservation implications of significant population differentiation in an endangered estuarine seahorse. Biodivers. Conserv. 26, 1275–1293 (2017).
    Article  Google Scholar 

    15.
    Vandewoestijne, S., Schtickzelle, N. & Baguette, M. Positive correlation between genetic diversity and fitness in a large, well-connected metapopulation. BMC Biol. 6, 46 (2008).
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Frankham, R. Genetic rescue of small inbred populations: meta-analysis reveals large and consistent benefits of gene flow. Mol. Ecol. 24, 2610–2618 (2015).
    PubMed  Article  Google Scholar 

    17.
    Nussear, K. E. et al. Translocation as a conservation tool for Agassiz’s desert tortoises: survivorship, reproduction, and movements. J. Wildl. Manag. 76, 1341–1353 (2012).
    Article  Google Scholar 

    18.
    Wright, D. J. et al. The impact of translocations on neutral and functional genetic diversity within and among populations of the Seychelles warbler. Mol. Ecol. 23, 2165–2177 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    19.
    Whiteley, A. R., Fitzpatrick, S. W., Funk, W. C. & Tallmon, D. A. Genetic rescue to the rescue. Trends Ecol. Evol. 30, 42–49 (2015).
    PubMed  Article  Google Scholar 

    20.
    Edmands, S. & Timmerman, C. C. Modeling factors affecting the severity of outbreeding depression. Conserv. Biol. 17, 883–892 (2003).
    Article  Google Scholar 

    21.
    Tallmon, D. A., Luikart, G. & Waples, R. S. The alluring simplicity and complex reality of genetic rescue. Trends Ecol. Evol. 19, 489–496 (2004).
    PubMed  Article  Google Scholar 

    22.
    Frankham, R. et al. Predicting the probability of outbreeding depression. Conserv. Biol. 25, 465–475 (2011).
    PubMed  Article  Google Scholar 

    23.
    Miller, K. A. et al. Securing the demographic and genetic future of tuatara through assisted colonization. Conserv. Biol. 26, 790–798 (2012).
    PubMed  Article  Google Scholar 

    24.
    Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).
    Article  Google Scholar 

    25.
    Peniche, G. et al. Protecting free-living dormice: molecular identification of cestode parasites in captive dormice (Muscardinus avellanarius) destined for reintroduction. EcoHealth 14, 106–116 (2017).
    PubMed  Article  Google Scholar 

    26.
    Pollom, R. Hippocampus capensis. The IUCN Red List of Threatened Species 2017: .T10056A54903534. http://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T10056A54903534.en (2017).

    27.
    Bell, E. M., Lockyear, J. F., McPherson, J. M., Marsden, A. D. & Vincent, A. C. J. First field studies of an endangered South African seahorse, Hippocampus capensis. Environ. Biol. Fishes 67, 35–46 (2003).
    Article  Google Scholar 

    28.
    Lockyear, J. F., Hecht, T., Kaiser, H. & Teske, P. R. The distribution and abundance of the endangered Knysna seahorse Hippocampus capensis (Pisces: Syngnathidae) in South African estuaries. Afr. J. Aquat. Sci. 31, 275–283 (2006).
    Article  Google Scholar 

    29.
    Teske, P. R., Cherry, M. I. & Matthee, C. A. Population genetics of the endangered Knysna seahorse, Hippocampus capensis. Mol. Ecol. 12, 1703–1715 (2003).
    CAS  PubMed  Article  Google Scholar 

    30.
    López, A., Vera, M., Planas, M. & Bouza, C. Conservation genetics of threatened Hippocampus guttulatus in vulnerable habitats in NW Spain: temporal and spatial stability of wild populations with flexible polygamous mating system in captivity. PLoS ONE 10, e0117538 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    31.
    Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    Lande, R. Genetics and demography in biological conservation. Science 241, 1455–1460 (1988).
    ADS  CAS  PubMed  Article  Google Scholar 

    33.
    Wang, J. Estimation of effective population sizes from data on genetic markers. Phil. Trans. R. Soc. B360, 1395–1409 (2005).
    Article  CAS  Google Scholar 

    34.
    Schwartz, M. K., Luikart, G. & Waples, R. S. Genetic monitoring as a promising tool for conservation and management. Trends Ecol. Evol. 22, 25–33 (2007).
    PubMed  Article  Google Scholar 

    35.
    Armstrong, D. P. & Seddon, P. J. Directions in reintroduction biology. Trends Ecol. Evol. 23, 20–25 (2008).
    PubMed  Article  Google Scholar 

    36.
    Cerón-Souza, I. et al. Contrasting demographic history and gene flow patterns of two mangrove species on either side of the Central American Isthmus. Ecol. Evol. 5, 3486–3499 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Woodall, L. C., Koldewey, H. J., Boehm, J. T. & Shaw, P. W. Past and present drivers of population structure in a small coastal fish, the European long snouted seahorse Hippocampus guttulatus. Conserv. Genet. 16, 1139–1153 (2015).
    Article  Google Scholar 

    38.
    Teske, P. R. et al. Molecular evidence for long-distance colonization in an Indo-Pacific seahorse lineage. Mar. Ecol. Prog. Ser. 286, 249–260 (2005).
    ADS  CAS  Article  Google Scholar 

    39.
    Heydorn, A. E. F. & Grindley, J. R. Estuaries of the Cape: Part II Synopses of available information on individual systems. Report 30. (Associated Printing and Publishing Co. (Pty) Ltd., 1985).

    40.
    Turpie, J. K. & Clark, B. Development of a conservation plan for temperate South African estuaries on the basis of biodiversity importance, ecosystem health and economic costs and benefits. Report by Anchor Environmental Consultants. C.A.P.E. Regional Estuarine Management Programme. 125 (2007).

    41.
    Penrith, M. J. & Penrith, M. Redescription of Pandaka silvana (Barnard) (Pisces, Gobiidae). Ann. South Afr. Mus. 60, 105–108 (1972).
    Google Scholar 

    42.
    Branch, G. M. The ecology of Patella linnaeus from the cape Peninsula, South Africa I. Zonation, movements and feeding. Zool. Afr. 6, 1–38 (1971).
    Article  Google Scholar 

    43.
    Largier, J. L., Attwood, C. & Harcourt-Baldwin, J. L. The hydrographic character of the Knysna Estuary. Trans. R. Soc. South Afr. 55, 107–122 (2000).
    Article  Google Scholar 

    44.
    Russell, I. A. Mass mortality of marine and estuarine fish in the Swartvlei and Wilderness lake systems, Southern Cape. South. Afr. J. Aquat. Sci. 20, 93–96 (1994).
    Google Scholar 

    45.
    Roberts, M. J., van der Lingen, C. D., Whittle, C. & van den Berg, M. Shelf currents, lee-trapped and transient eddies on the inshore boundary of the Agulhas Current, South Africa: their relevance to the KwaZulu-Natal sardine run. Afr. J. Mar. Sci. 32, 423–447 (2010).
    Article  Google Scholar 

    46.
    Teske, P. R., Bader, S. & Golla, T. R. Passive dispersal against an ocean current. Mar. Ecol. Prog. Ser. 539, 153–163 (2015).
    ADS  CAS  Article  Google Scholar 

    47.
    Claassens, L. An artificial water body provides habitat for an endangered estuarine seahorse species. Estuar. Coast. Shelf Sci. 180, 1–10 (2016).
    ADS  Article  Google Scholar 

    48.
    Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A. & Losos, E. Quantifying threats to imperiled species in the United States: Assessing the relative importance of habitat destruction, alien species, pollution, overexploitation, and disease. Bioscience 48, 607–615 (1998).
    Article  Google Scholar 

    49.
    Hey, J. Isolation with migration models for more than two populations. Mol. Biol. Evol. 27, 905–920 (2010).
    CAS  PubMed  Article  Google Scholar 

    50.
    Claassens, L., Barnes, R. S. K., Wasserman, J., Lamberth, S. J., Miranda, A. F., van Niekerk, L. & Adams, J. B. Knysna Estuary health: ecological status, threats and options for the future. Afr. J. Aquat. 45 (2020).

    51.
    Nielsen, R. & Wakeley, J. Distinguishing migration from isolation: a Markov chain Monte Carlo approach. Genetics 158, 885–896 (2001).
    CAS  PubMed  PubMed Central  Google Scholar 

    52.
    Whitfield, A. K. Threatened fishes of the world: Hippocampus capensis Boulenger, 1900 (Syngnathidae). Environ. Biol. Fishes 44, 362–362 (1995).
    Article  Google Scholar 

    53.
    Yue, G. H., David, L. & Orban, L. Mutation rate and pattern of microsatellites in common carp (Cyprinus carpio L.). Genetica 129, 329–331 (2007).
    CAS  PubMed  Article  Google Scholar 

    54.
    Waples, R. S. & Do, C. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262 (2010).
    PubMed  Article  Google Scholar 

    55.
    Do, C. et al. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).
    CAS  PubMed  Article  Google Scholar 

    56.
    Heled, J. & Drummond, A. J. Bayesian inference of population size history from multiple loci. BMC Evol. Biol. 8, 289 (2008).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    57.
    Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    58.
    Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Lischer, H. E. L. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299 (2012).
    CAS  PubMed  Article  Google Scholar 

    60.
    Rambaut, A., Suchard, M. A., Xie, D. & Drummond, A. J. Tracer v1.6. (2014). More

  • in

    Marauding plants steer clear of a communist-ruled island

    Cuba has hosted relatively small numbers of tourist groups given its size, which might have helped to keep invasive plants at bay. Credit: Roberto Machado Noa/LightRocket/Getty

    Ecology
    18 February 2021

    Cuba’s relatively closed economy could explain why it has fewer invasive plant species per unit area than other Caribbean islands.

    For more than 60 years, the rocky relationship between the United States and Cuba has helped to steer tourists and businesses away from the Caribbean island. Now, researchers have found that Cuba’s economic and political isolation might also have limited the spread of invasive plants.
    Meghan Brown at Hobart and William Smith Colleges in Geneva, New York, and her colleagues estimated the number of invasive plant species on 45 Caribbean islands. The researchers found that larger islands tend to have more exotic plant species than do smaller ones. But Cuba, the biggest island in the Caribbean, is home to hundreds fewer such species than expected for its size.
    Mass tourism seems to favour the introduction of invasive plants, the team found, probably because hotels plant exotic ornamental species and tourists carry seeds in their bags or on their shoes. Cuba — which has one of the region’s lowest shares of holidaymakers in comparison to its area — has about the same number of invasive species as Puerto Rico, which is one-tenth the size of Cuba but has many more visitors for its land area. More

  • in

    Dogs (Canis familiaris) recognize their own body as a physical obstacle

    1.
    Bahrick, L. E. & Watson, J. S. Detection of intermodal proprioceptive–visual contingency as a potential basis of self-perception in infancy. Dev. Psychol. 21, 963 (1985).
    Article  Google Scholar 
    2.
    Van Den Bos, E. & Jeannerod, M. Sense of body and sense of action both contribute to self-recognition. Cognition 85, 177–187 (2002).
    Article  Google Scholar 

    3.
    Wilson, M. Six views of embodied cognition. Psychon. B. Rev. 9, 625–636 (2002).
    Article  Google Scholar 

    4.
    Smith, L. & Gasser, M. The development of embodied cognition: Six lessons from babies. Artif. life 11, 13–29 (2005).
    Article  Google Scholar 

    5.
    Shettleworth, S. J. Cognition, Evolution, and Behavior. Oxford University Press.

    6.
    Kohda, M. et al. If a fish can pass the mark test, what are the implications for consciousness and self-awareness testing in animals?. PLoS Biol 17, e3000021 (2019).
    CAS  Article  Google Scholar 

    7.
    Gallup, G. G. Chimpanzees: Self-recognition. Science 167, 86–87 (1970).
    ADS  Article  Google Scholar 

    8.
    Epstein, R., Lanza, R. P. & Skinner, B. F. “Self-awareness” in the pigeon. Science 212, 695–696 (1981).
    ADS  CAS  Article  Google Scholar 

    9.
    Heyes, C. M. Self-recognition in primates: Further reflections create a hall of mirrors. Anim. Behav. 50, 1533–1542 (1995).
    Article  Google Scholar 

    10.
    Suddendorf, T. & Butler, D. L. Response to Gallup et al.: Are rich interpretations of visual self-recognition a bit too rich?. Trends. Cogn. Sci. 18, 58–59 (2014).
    Article  Google Scholar 

    11.
    Reiss, D. & Marino, L. Mirror self-recognition in the bottlenose dolphin: A case of cognitive convergence. Proc. Natl. Acad. Sci. USA 98, 5937–5942 (2001).
    ADS  CAS  Article  Google Scholar 

    12.
    Plotnik, J. M., De Waal, F. B. & Reiss, D. Self-recognition in an Asian elephant. Proc. Natl. Acad. Sci. USA 103, 17053–17057 (2006).
    ADS  CAS  Article  Google Scholar 

    13.
    Prior, H., Schwarz, A. & Güntürkün, O. Mirror-induced behavior in the magpie (Pica pica): Evidence of self-recognition. PLoS Biol 6, e202 (2008).
    Article  Google Scholar 

    14.
    Bekoff, M. & Sherman, P. W. Reflections on animal selves. Trends Ecol. Evol. 19, 176–180 (2004).
    Article  Google Scholar 

    15.
    Lenkei, R., Faragó, T., Kovács, D., Zsilák, B. & Pongrácz, P. That dog won’t fit: Body size awareness in dogs. Anim. Cogn 23, 337–350 (2019).
    Article  Google Scholar 

    16.
    Zazzo, R. Des enfants, des singes et des chiens devant le miroir. Rev. Psychol. Appl. 29, 235–246 (1979).
    Google Scholar 

    17.
    Cuthill, I. & Guilford, T. Perceived risk and obstacle avoidance in flying birds. Anim. Behav. 40, 188–190 (1990).
    Article  Google Scholar 

    18.
    Khvatov, I. A., Sokolov, A. Y. & Kharitonov, A. N. Snakes Elaphe radiata may acquire awareness of their body limits when trying to hide in a shelter. Behav. Sci. 9, 67 (2019).
    Article  Google Scholar 

    19.
    Maeda, T. & Fujita, K. Do dogs (Canis familiaris) recognize their own body size? In Proceedings of the 2nd Canine Science Forum, Vienna, Austria, 52 (2010).

    20.
    Dale, R. & Plotnik, J. M. Elephants know when their bodies are obstacles to success in a novel transfer task. Sci. Rep. 7, 46309 (2017).
    ADS  CAS  Article  Google Scholar 

    21.
    Brownell, C. A., Zerwas, S. & Ramani, G. B. “So big”: The development of body self-awareness in toddlers. Child Dev. 78, 1426–1440 (2007).
    Article  Google Scholar 

    22.
    Povinelli, D. J. & Cant, J. G. Arboreal clambering and the evolution of self-conception. Q. Rev. Biol. 70, 393–421 (1995).
    CAS  Article  Google Scholar 

    23.
    Povinelli, D. J. Failure to find self-recognition in Asian elephants (Elephas maximus) in contrast to their use of mirror cues to discover hidden food. J. Comp. Psychol. 103, 122 (1989).
    Article  Google Scholar 

    24.
    Topál, J. et al. The dog as a model for understanding human social behaviour. Adv. Stud. Behav. 39, 71–116 (2009).
    Article  Google Scholar 

    25.
    Sanford, E. M., Burt, E. R. & Meyers-Manor, J. E. Timmy’s in the well: Empathy and prosocial helping in dogs. Learn. Behav. 46, 374–386 (2018).
    Article  Google Scholar 

    26.
    Pongrácz, P., Bánhegyi, P. & Miklósi, Á. When rank counts—dominant dogs learn better from a human demonstrator in a two-action test. Behaviour 149, 111–132 (2012).
    Article  Google Scholar 

    27.
    Huber, L., Popovová, N., Riener, S., Salobir, K. & Cimarelli, G. Would dogs copy irrelevant actions from their human caregiver?. Learn. Behav. 46, 387–397 (2018).
    Article  Google Scholar 

    28.
    Virányi, Z. S., Topál, J., Miklósi, Á. & Csányi, V. A nonverbal test of knowledge attribution: A comparative study on dogs and children. Anim. Cogn. 9, 13–26 (2006).
    Article  Google Scholar 

    29.
    Polgárdi, R., Topál, J. & Csányi, V. Intentional behaviour in dog-human communication: An experimental analysis of “showing” behaviour in the dog. Anim. Cogn. 3, 159–166 (2000).
    Article  Google Scholar 

    30.
    Pongrácz, P., Hegedüs, D., Sanjurjo, B., Kővári, A. & Miklósi, Á. “We will work for you”—Social influence may suppress individual food preferences in a communicative situation in dogs. Learn. Motiv. 44, 270–281 (2013).
    Article  Google Scholar 

    31.
    Fugazza, C., Pogány, Á. & Miklósi, Á. Recall of others’ actions after incidental encoding reveals episodic-like memory in dogs. Curr. Biol. 26, 3209–3213 (2016).
    CAS  Article  Google Scholar 

    32.
    Horowitz, A. Smelling themselves: Dogs investigate their own odours longer when modified in an “olfactory mirror” test. Behav. Proc. 143, 17–24 (2017).
    Article  Google Scholar 

    33.
    Moore, C., Mealiea, J., Garon, N. & Povinelli, D. J. The development of body self-awareness. Infancy 11, 157–174 (2007).
    Article  Google Scholar 

    34.
    Howell, T. J. & Bennett, P. C. Can dogs (Canis familiaris) use a mirror to solve a problem?. J. Vet. Behav. 6, 306–312 (2011).
    Article  Google Scholar 

    35.
    Bekoff, M. Awareness: Animal reflections. Nature 419, 255 (2002).
    ADS  CAS  Article  Google Scholar 

    36.
    Kaplan, J. T., Aziz-Zadeh, L., Uddin, L. Q. & Iacoboni, M. The self across the senses: An fMRI study of self-face and self-voice recognition. Soc. Cogn. Affect. Neur. 3, 218–223 (2008).
    Article  Google Scholar  More

  • in

    Lifestyle of sponge symbiont phages by host prediction and correlative microscopy

    1.
    Wommack KE, Colwell RR. Virioplankton: viruses in aquatic ecosystems. Microbiol Mol Biol Rev. 2000;64:69–114.
    CAS  PubMed  PubMed Central  Article  Google Scholar 
    2.
    Keen EC, Dantas G. Close encounters of three kinds: bacteriophages, commensal bacteria, and host immunity. Trends Microbiol. 2018;26:943–54.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    3.
    Sausset R, Petit MA, Gaboriau-Routhiau V, De Paepe M. New insights into intestinal phages. Mucosal Immunol. 2020;13:205–15.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    4.
    Van Belleghem J, Dąbrowska K, Vaneechoutte M, Barr J, Bollyky P. Interactions between bacteriophage, bacteria, and the mammalian immune system. Viruses. 2018;11:205–15.
    Google Scholar 

    5.
    Wilhelm SW, Suttle CA. Viruses and nutrient cycles in the sea: viruses play critical roles in the structure and function of aquatic food webs. BioScience. 1999;49:781–8.
    Article  Google Scholar 

    6.
    Thingstad TF. Elements of a theory for the mechanisms controlling abundance, diversity, and biogeochemical role of lytic bacterial viruses in aquatic systems. Limnol Oceanogr. 2000;45:1320–2.
    Article  Google Scholar 

    7.
    Winter C, Bouvier T, Weinbauer MG, Thingstad TF. Trade-offs between competition and defense specialists among unicellular planktonic organisms: the “killing the winner” hypothesis revisited. Microbiol Mol Biol Rev. 2010;74:42–57.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    8.
    Minot S, Bryson A, Chehoud C, Wu GD, Lewis JD, Bushman FD. Rapid evolution of the human gut virome. PNAS. 2013;110:12450–5.
    CAS  PubMed  Article  Google Scholar 

    9.
    Thurber RV, Haynes M, Breitbart M, Wegley L, Rohwer F. Laboratory procedures to generate viral metagenomes. Nat Protocols. 2009;4:470–83..

    10.
    Leigh BA, Bordenstein SR, Brooks AW, Mikaelyan A, Bordenstein SR. Finer-scale phylosymbiosis: insights from insect viromes. mSystems. 2018a;3:e00131–18.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    11.
    Wille M, Shi M, Klaassen M, Hurt AC, Holmes EC. Virome heterogeneity and connectivity in waterfowl and shorebird communities. ISME J. 2019;13:2603–16.
    PubMed  PubMed Central  Article  Google Scholar 

    12.
    Jahn MT, Arkhipova K, Markert SM, Stigloher C, Lachnit T, Pita L et al. A phage protein aids bacterial symbionts in eukaryote immune evasion. Cell Host Microbe. 2019;26:542–50.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Leigh BA, Djurhuus A, Breitbart M, Dishaw LJ. The gut virome of the protochordate model organism, Ciona intestinalis subtype A. Virus Res. 2018b;244:137–46.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Shkoporov AN, Clooney AG, Sutton TDS, Ryan FJ, Daly KM, Nolan JA, et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe. 2019;26:527–41.e525.
    CAS  Article  Google Scholar 

    15.
    Fuhrman JA. Marine viruses and their biogeochemical and ecological effects. Nature. 1999;399:541–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Paul JH. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? ISME J. 2008;2:579–89.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Touchon M, Bernheim A, Rocha EP. Genetic and life-history traits associated with the distribution of prophages in bacteria. ISME J. 2016;10:2744–54.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    18.
    Howard-Varona C, Hargreaves KR, Abedon ST, Sullivan MB. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 2017;11:1511–20.
    PubMed  PubMed Central  Article  Google Scholar 

    19.
    Weitz JS. Quantitative viral ecology dynamics of viruses and their microbial hosts. Princeton: Princeton University Press; 2015.

    20.
    Bondy-Denomy J, Qian J, Westra ER, Buckling A, Guttman DS, Davidson AR, et al. Prophages mediate defense against phage infection through diverse mechanisms. ISME J. 2016;10:2854–66.
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Herold S, Karch H, Schmidt H. Shiga toxin-encoding bacteriophages–genomes in motion. Int J Med Microbiol. 2004;294:115–21.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Kim M-S, Bae J-W. Lysogeny is prevalent and widely distributed in the murine gut microbiota. ISME J. 2018;12:1127–41.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    23.
    Reyes A, Wu M, McNulty NP, Rohwer FL, Gordon JI. Gnotobiotic mouse model of phage–bacterial host dynamics in the human gut. PNAS. 2013;110:20236–41.

    24.
    Bonilla-Rosso G, Steiner T, Wichmann F, Bexkens E, Engel P. Honey bees harbor a diverse gut virome engaging in nested strain-level interactions with the microbiota. PNAS. 2020;117:7355–62.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    25.
    Sweere JM, Van Belleghem JD, Ishak H, Bach MS, Popescu M, Sunkari V, et al. Bacteriophage trigger antiviral immunity and prevent clearance of bacterial infection. Science. 2019;363:eaat9691.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    26.
    Hadas E, Marie D, Shpigel M, Ilan M. Virus predation by sponges is a new nutrient-flow pathway in coral reef food webs. Limnol Oceanogr. 2006;51:1548–50.
    Article  Google Scholar 

    27.
    Rix L, Ribes M, Coma R, Jahn MT, de Goeij JM, van Oevelen D, et al. Heterotrophy in the earliest gut: a single-cell view of heterotrophic carbon and nitrogen assimilation in sponge-microbe symbioses. ISME J. 2020;14:2554–67.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    28.
    Lurgi M, Thomas T, Wemheuer B, Webster NS, Montoya JM. Modularity and predicted functions of the global sponge-microbiome network. Nat Commun. 2019; 10. https://doi.org/10.1038/s41467-019-08925-4.

    29.
    Reveillaud J, Maignien L, Eren AM, Huber JA, Apprill A, Sogin ML, et al. Host-specificity among abundant and rare taxa in the sponge microbiome. ISME J. 2014;8:1198–209.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    30.
    Laffy PW, Wood-Charlson EM, Turaev D, Jutz S, Pascelli C, Botte ES, et al. Reef invertebrate viromics: diversity, host specificity and functional capacity. Environ Microbiol. 2018;20:2125–41.
    PubMed  Article  Google Scholar 

    31.
    Taylor MW, Radax R, Steger D, Wagner M. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol Mol Biol Rev. 2007;71:295–+.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    Pascelli C, Laffy PW, Botté E, Kupresanin M, Rattei T, Lurgi M, et al. Viral ecogenomics across the Porifera. Microbiome. 2020;8:144.
    PubMed  PubMed Central  Article  Google Scholar 

    33.
    Allers E, Moraru C, Duhaime MB, Beneze E, Solonenko N, Barrero-Canosa J, et al. Single-cell and population level viral infection dynamics revealed by phageFISH, a method to visualize intracellular and free viruses. Environ Microbiol. 2013;15:2306–18.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    34.
    Edwards RA, McNair K, Faust K, Raes J, Dutilh BE. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol Rev. 2016;40:258–72.
    CAS  PubMed  Article  Google Scholar 

    35.
    Horn H, Slaby BM, Jahn MT, Bayer K, Moitinho-Silva L, Forster F, et al. An enrichment of CRISPR and other defense-related features in marine sponge-associated microbial metagenomes. Front Microbiol. 2016;7:1751.
    PubMed  PubMed Central  Google Scholar 

    36.
    Slaby BM, Hackl T, Horn H, Bayer K, Hentschel U. Metagenomic binning of a marine sponge microbiome reveals unity in defense but metabolic specialization. ISME J. 2017;11:2465.
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Fiore CL, Labrie M, Jarett JK, Lesser MP. Transcriptional activity of the giant barrel sponge, Xestospongia muta Holobiont: molecular evidence for metabolic interchange. Front Microbiol. 2015; 6. https://doi.org/10.3389/fmicb.2015.00364.

    38.
    Ryu T, Seridi L, Moitinho-Silva L, Oates M, Liew YJ, Mavromatis C, et al. Hologenome analysis of two marine sponges with different microbiomes. BMC Genom. 2016;17:1–11.
    Article  CAS  Google Scholar 

    39.
    Tully BJ, Sachdeva R, Graham ED, Heidelberg JF. 290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology. PeerJ. 2017;5:e3558.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    40.
    Biswas A, Staals RHJ, Morales SE, Fineran PC, Brown CM. CRISPRDetect: a flexible algorithm to define CRISPR arrays. BMC Genom. 2016;17:356–356.
    Article  CAS  Google Scholar 

    41.
    Biswas A, Gagnon JN, Brouns SJ, Fineran PC, Brown CM. CRISPRTarget: bioinformatic prediction and analysis of crRNA targets. RNA Biol. 2013;10:817–27.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    42.
    Burstein D, Harrington LB, Strutt SC, Probst AJ, Anantharaman K, Thomas BC, et al. New CRISPR–Cas systems from uncultivated microbes. Nature. 2016;542:237.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    43.
    Lowe TM, Chan PP. tRNAscan-SE On-line: integrating search and context for analysis of transfer RNA genes. Nucleic Acids Res. 2016;44:W54–57.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    McNair K, Bailey BA, Edwards RA. PHACTS, a computational approach to classifying the lifestyle of phages. Bioinformatics. 2012;28:614–8.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    46.
    Grazziotin AL, Koonin EV, Kristensen DM. Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation. Nucleic Acids Res. 2017;45:D491–8.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    47.
    Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    48.
    Jahn MT, Markert SM, Ryu T, Ravasi T, Stigloher C, Hentschel U, et al. Shedding light on cell compartmentation in the candidate phylum Poribacteria by high resolution visualisation and transcriptional profiling. Sci Rep. 2016;6:35860.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    49.
    Chin CR, Perreira JM, Savidis G, Portmann JM, Aker AM, Feeley EM, et al. Direct visualization of HIV-1 replication intermediates shows that capsid and CPSF6 modulate HIV-1 intra-nuclear invasion and integration. Cell Rep. 2015;13:1717–31.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    50.
    Reynolds ES. The use of lead citrate at high pH as an electron-opaque stain in electron microscopy. J Cell Biol. 1963;17:208–12.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    51.
    Paul-Gilloteaux P, Heiligenstein X, Belle M, Domart MC, Larijani B, Collinson L, et al. eC-CLEM: flexible multidimensional registration software for correlative microscopies. Nat Methods. 2017;14:102–3.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    52.
    Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    53.
    R Development Core Team. R: a language and environment for statistical computing. In: Computing RFfS (ed): Vienna, Austria 2020.

    54.
    Bayer K, Jahn MT, Slaby BM, Moitinho-Silva L, Hentschel U. Marine sponges as Chloroflexi hot spots: genomic insights and high-resolution visualization of an abundant and diverse symbiotic clade. mSystems. 2018;3:e00150–18.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    55.
    Thomas T, Moitinho-Silva L, Lurgi M, Bjork JR, Easson C, Astudillo-Garcia C, et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Commun. 2016;7:11870.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    Lima-Mendez G, Van Helden J, Toussaint A, Leplae R. Reticulate representation of evolutionary and functional relationships between phage genomes. Mol Biol Evol. 2008;25:762–77.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Bavestrello G, Burlando B, Sara M. The architecture of the canal systems of Petrosia ficiformis and Chondrosia reniformis studied by corrosion casts (Porifera, Demospongiae). Zoomorphology. 1988;108:161–6.
    Article  Google Scholar 

    58.
    Van Soest RWM, Boury-Esnault N, Hooper JNA, Rützler K, de Voogd NJ, Alvarez B et al. World porifera database. 2019.

    59.
    Oh JH, Alexander LM, Pan M, Schueler KL, Keller MP, Attie AD, et al. Dietary fructose and microbiota-derived short-chain fatty acids promote bacteriophage production in the gut symbiont Lactobacillus reuteri. Cell Host Microbe. 2019;25:273–84 e276.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    De Paepe M, Tournier L, Moncaut E, Son O, Langella P, Petit MA. Carriage of lambda latent virus is costly for its bacterial host due to frequent reactivation in monoxenic mouse intestine. PLoS Genet. 2016;12:e1005861.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    61.
    Barr JJ, Auro R, Furlan M, Whiteson KL, Erb ML, Pogliano J, et al. Bacteriophage adhering to mucus provide a non-host-derived immunity. PNAS. 2013;110:10771–6.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    62.
    Roux S. A viral ecogenomics framework to uncover the secrets of nature’s “microbe whisperers”. mSystems. 2019;4:e00111–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    63.
    Paez-Espino D, Roux S, Chen IA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes. Nucleic Acids Res. 2019;47:D678–86.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    64.
    Deng L, Ignacio-Espinoza JC, Gregory AC, Poulos BT, Weitz JS, Hugenholtz P, et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature. 2014;513:242–45.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    65.
    Džunková M, Low SJ, Daly JN, Deng L, Rinke C, Hugenholtz P. Defining the human gut host–phage network through single-cell viral tagging. Nat Microbiol. 2019;4:2192–203.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    66.
    Marbouty M, Baudry L, Cournac A, Koszul R. Scaffolding bacterial genomes and probing host-virus interactions in gut microbiome by proximity ligation (chromosome capture) assay. Sci Adv. 2017;3:e1602105.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    67.
    Sullivan MB, Waterbury JB, Chisholm SW. Cyanophages infecting the oceanic cyanobacterium Prochlorococcus. Nature. 2003;424:1047–51.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    68.
    de Jonge PA, Nobrega FL, Brouns SJJ, Dutilh BE. Molecular and evolutionary determinants of bacteriophage host range. Trends Microbiol. 2019;27:51–63.
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    69.
    Kauffman KM, Hussain FA, Yang J, Arevalo P, Brown JM, Chang WK, et al. A major lineage of non-tailed dsDNA viruses as unrecognized killers of marine bacteria. Nature. 2018;554:118–22.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    70.
    Flores CO, Valverde S, Weitz JS. Multi-scale structure and geographic drivers of cross-infection within marine bacteria and phages. ISME J. 2013;7:520–32.
    PubMed  Article  PubMed Central  Google Scholar 

    71.
    Soffer N, Zaneveld J, Vega Thurber R. Phage-bacteria network analysis and its implication for the understanding of coral disease. Environ Microbiol. 2015;17:1203–18.
    CAS  Article  Google Scholar 

    72.
    Tzipilevich E, Habusha M, Ben-Yehuda S. Acquisition of phage sensitivity by bacteria through exchange of phage receptors. Cell. 2017;168:186–99 e112.
    CAS  PubMed  Article  Google Scholar 

    73.
    Battich N, Stoeger T, Pelkmans L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat Methods. 2013;10:1127–33.
    CAS  PubMed  Article  Google Scholar 

    74.
    Li X-Y, Lachnit T, Fraune S, Bosch TCG, Traulsen A, Sieber M. Temperate phages as self-replicating weapons in bacterial competition. J R Soc Interface. 2017;14:20170563.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    75.
    Pascelli C, Laffy PW, Kupresanin M, Ravasi T, Webster NS. Morphological characterization of virus-like particles in coral reef sponges. PeerJ. 2018;6:e5625–5625.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    76.
    Sime-Ngando T. Environmental bacteriophages: viruses of microbes in aquatic ecosystems. Front Microbiol. 2014;5:355.
    PubMed  PubMed Central  Article  Google Scholar 

    77.
    Knowles B, Silveira CB, Bailey BA, Barott K, Cantu VA, Cobián-Güemes AG, et al. Lytic to temperate switching of viral communities. Nature. 2016;531:466.
    CAS  PubMed  Article  Google Scholar 

    78.
    Duerkop BA, Clements CV, Rollins D, Rodrigues JLM, Hooper LV. A composite bacteriophage alters colonization by an intestinal commensal bacterium. PNAS. 2012;109:17621–6.

    79.
    Thingstad TF, Vage S, Storesund JE, Sandaa RA, Giske J. A theoretical analysis of how strain-specific viruses can control microbial species diversity. PNAS. 2014;111:7813–8.
    CAS  PubMed  Article  Google Scholar 

    80.
    Morella NM, Gomez AL, Wang G, Leung MS, Koskella B. The impact of bacteriophages on phyllosphere bacterial abundance and composition. Mol Ecol. 2018;27:2025–38.
    PubMed  Article  PubMed Central  Google Scholar 

    81.
    Wattam AR, Davis JJ, Assaf R, Boisvert S, Brettin T, Bun C, et al. Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center. Nucleic Acids Res. 2017;45:D535–42.
    CAS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Large-scale farmer-led experiment demonstrates positive impact of cover crops on multiple soil health indicators

    1.
    Seifert, C. A., Azzari, G. & Lobell, D. B. Satellite detection of cover crops and their effects on crop yield in the Midwestern United States. Environ. Res. Lett. 13, 064033 (2018).
    ADS  Article  Google Scholar 
    2.
    2017 Census of Agriculture, Summary and State Data (USDA, 2019); https://www.nass.usda.gov/Publications/AgCensus/2017/Full_Report/Volume_1,_Chapter_1_US/usv1.pdf

    3.
    Basche, A. D. et al. Soil water improvements with the long-term use of a winter rye cover crop. Agric. Water Manag. 172, 40–50 (2016).
    Article  Google Scholar 

    4.
    Schipanski, M. E. et al. A framework for evaluating ecosystem services provided by cover crops in agroecosystems. Agric. Syst. 125, 12–22 (2014).
    Article  Google Scholar 

    5.
    Blanco-Canqui, H. et al. Cover crops and ecosystem services: insights from studies in temperate soils. Agron. J. 107, 2449–2474 (2015).
    CAS  Article  Google Scholar 

    6.
    Andrews, S. S. et al. On‐farm assessment of soil quality in California’s central valley. Agron. J. 94, 12–23 (2002).
    Article  Google Scholar 

    7.
    Welch, R. Y., Behnke, G. D., Davis, A. S., Masiunas, J. & Villamil, M. B. Using cover crops in headlands of organic grain farms: effects on soil properties, weeds and crop yields. Agric. Ecosyst. Environ. 216, 322–332 (2016).
    Article  Google Scholar 

    8.
    Wyland, L. Winter cover crops in a vegetable cropping system: impacts on nitrate leaching, soil water, crop yield, pests and management costs. Agric. Ecosyst. Environ. 59, 1–17 (1996).
    Article  Google Scholar 

    9.
    Karlen, D. L. & Doran, J. W. Cover crop management effects on soybean and corn growth and nitrogen dynamics in an on-farm study. Am. J. Altern. Agric. 6, 71–82 (1991).
    Article  Google Scholar 

    10.
    Koch, R. L. et al. On-farm evaluation of a fall-seeded rye cover crop for suppression of soybean aphid (Hemiptera: Aphididae) on soybean: suppression of soybean aphid with rye cover crop. Agric. For. Entomol. 17, 239–246 (2015).
    Article  Google Scholar 

    11.
    Sayre, N. F., deBuys, W., Bestelmeyer, B. T. & Havstad, K. M. “The Range Problem” after a century of rangeland science: new research themes for altered landscapes. Rangeland Ecol. Manag. 65, 545–552 (2012).
    Article  Google Scholar 

    12.
    Kladivko, E. J. et al. State-wide soil health programs for education and on-farm assessment: lessons learned. J. Soil Water Conserv. 74, 12A–17A (2019).
    Article  Google Scholar 

    13.
    Poeplau, C. & Don, A. Carbon sequestration in agricultural soils via cultivation of cover crops – a meta-analysis. Agric. Ecosyst. Environ. 200, 33–41 (2015).
    CAS  Article  Google Scholar 

    14.
    Vermeulen, S. et al. A global agenda for collective action on soil carbon. Nat. Sustain. 2, 2–4 (2019).
    Article  Google Scholar 

    15.
    Lehmann, J., Bossio, D. A., Kögel-Knabner, I. & Rillig, M. C. The concept and future prospects of soil health. Nat. Rev. Earth Environ. 1, 544–553 (2020).
    ADS  PubMed  Article  Google Scholar 

    16.
    Stewart, R. D. et al. What we talk about when we talk about soil health. Agric. Environ. Lett. 3, 180033 (2018).
    Article  CAS  Google Scholar 

    17.
    Norris, C. E. et al. Introducing the North American project to evaluate soil health measurements. Agron. J. 112, 3195–3215 (2020).
    Article  Google Scholar 

    18.
    Sanderman, J., Savage, K. & Dangal, S. R. S. Mid‐infrared spectroscopy for prediction of soil health indicators in the United States. Soil Sci. Soc. Am. J. 84, 251–261 (2020).
    ADS  CAS  Article  Google Scholar 

    19.
    Rorick, J. D. & Kladivko, E. J. Cereal rye cover crop effects on soil carbon and physical properties in Southeastern Indiana. J. Soil Water Conserv. 72, 260–265 (2017).
    Article  Google Scholar 

    20.
    Faé, G. S. et al. Integrating winter annual forages into a no-till corn silage system. Agron. J. 101, 1286–1296 (2009).
    Article  Google Scholar 

    21.
    Wegner, B. R. et al. Soil response to corn residue removal and cover crops in eastern South Dakota. Soil Sci. Soc. Am. J. 79, 1179–1187 (2015).
    ADS  CAS  Article  Google Scholar 

    22.
    Karlen, D. L., Goeser, N. J., Veum, K. S. & Yost, M. A. On-farm soil health evaluations: challenges and opportunities. J. Soil Water Conserv. 72, 26A–31A (2017).
    Article  Google Scholar 

    23.
    Wade, J. et al. Improved soil biological health increases corn grain yield in N fertilized systems across the Corn Belt. Sci. Rep. 10, 3917 (2020).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Bossio, D. A. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).
    Article  Google Scholar 

    25.
    Stanton, C. Y. et al. Managing cropland and rangeland for climate mitigation: an expert elicitation on soil carbon in California. Clim. Change 147, 633–646 (2018).
    ADS  CAS  Article  Google Scholar 

    26.
    Lugato, E., Leip, A. & Jones, A. Mitigation potential of soil carbon management overestimated by neglecting N2O emissions. Nat. Clim. Change 8, 219–223 (2018).
    ADS  CAS  Article  Google Scholar 

    27.
    Kaye, J. P. & Quemada, M. Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev. 37, 4 (2017).
    Article  Google Scholar 

    28.
    Basche, A. D. & DeLonge, M. S. Comparing infiltration rates in soils managed with conventional and alternative farming methods: a meta-analysis. PLoS ONE 14, e0215702 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    Basche, A. & DeLonge, M. The impact of continuous living cover on soil hydrologic properties: a meta-analysis. Soil Sci. Soc. Am. J. 81, 1179–1190 (2017).
    ADS  CAS  Article  Google Scholar 

    30.
    Roper, W. R., Osmond, D. L. & Heitman, J. L. A response to “Reanalysis validates soil health indicator sensitivity and correlation with long‐term crop yields”. Soil Sci. Soc. Am. J. 83, 1842–1845 (2019).
    ADS  CAS  Article  Google Scholar 

    31.
    King, A. E., Ali, G. A., Gillespie, A. W. & Wagner-Riddle, C. Soil organic matter as catalyst of crop resource capture. Front. Environ. Sci. 8, 50 (2020).
    Article  Google Scholar 

    32.
    Oldfield, E. E., Bradford, M. A. & Wood, S. A. Global meta-analysis of the relationship between soil organic matter and crop yields. SOIL 5, 15–32 (2019).
    CAS  Article  Google Scholar 

    33.
    Oldfield, E. E., Wood, S. A. & Bradford, M. A. Direct evidence using a controlled greenhouse study for threshold effects of soil organic matter on crop growth. Ecol. Appl. 30, e02073 (2020).
    PubMed  Article  Google Scholar 

    34.
    Wood, S. A. et al. Opposing effects of different soil organic matter fractions on crop yields. Ecol. Appl. 26, 2072–2085 (2016).
    PubMed  Article  Google Scholar 

    35.
    Fine, A. K., van Es, H. M. & Schindelbeck, R. R. Statistics, scoring functions, and regional analysis of a comprehensive soil health database. Soil Sci. Soc. Am. J. 81, 589 (2017).
    ADS  CAS  Article  Google Scholar 

    36.
    Fine, A. K., Ristow, A., Schindelbeck, R. R. & van Es, H. M. Update of scoring functions for Cornell Soil Health Test. What’s Cropping Up? Blog https://blogs.cornell.edu/whatscroppingup/2016/11/30/update-of-scoring-functions-for-cornell-soil-health-test/ (2016).

    37.
    Bradford, M. A. et al. Discontinuity in the responses of ecosystem processes and multifunctionality to altered soil community composition. Proc. Natl Acad. Sci. USA 111, 14478–14483 (2014).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    38.
    Bradford, M. A. et al. Reply to Byrnes et al.: Aggregation can obscure understanding of ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 111, E5491 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Kettler, T. A., Doran, J. W. & Gilbert, T. L. Simplified method for soil particle-size determination to accompany soil-quality analyses. Soil Sci. Soc. Am. J. 65, 849–852 (2001).
    ADS  CAS  Article  Google Scholar 

    40.
    Moebius, B. N. et al. Evaluation of laboratory-measured soil properties as indicators of soil physical quality. Soil Sci. 172, 895–912 (2007).
    ADS  CAS  Article  Google Scholar 

    41.
    Reynolds, W. & Topp, G. in Soil Sampling and Methods of Analysis (eds Carter, M. R. & Gregorich, E. G.) 981–997 (CRC Press, 2008).

    42.
    Nelson, D. & Sommers, D. in Methods of Soil Analysis. Part 3 (Sparks, D. L., Page, A. L., Helmke, P. A. & Loeppert, R. H.) 961–1010 (Soil Science Society of America, 1996).

    43.
    Weil, R. R., Islam, K. R., Stine, M. A., Gruver, J. B. & Samson-Liebig, S. E. Estimating active carbon for soil quality assessment: a simplified method for laboratory and field use. Am. J. Altern. Agric. 18, 3–17 (2003).
    Article  Google Scholar 

    44.
    Haney, R. L. & Haney, E. B. Simple and rapid laboratory method for rewetting dry soil for incubations. Commun. Soil Sci. Plant Anal. 41, 1493–1501 (2010).
    CAS  Article  Google Scholar 

    45.
    Wright, S. F. & Upadhyaya, A. Extraction of an abundant and unusual protein from soil and comparison with hyphal protein of arbuscular mycorrhizal fungi. Soil Sci. 161, 575–586 (1996).
    ADS  CAS  Article  Google Scholar 

    46.
    Bunnefeld, N. & Phillimore, A. B. Island, archipelago and taxon effects: mixed models as a means of dealing with the imperfect design of nature’s experiments. Ecography 35, 15–22 (2012).
    Article  Google Scholar 

    47.
    Gelman, A. Scaling regression inputs by dividing by two standard deviations. Stat. Med. 27, 2865–2873 (2008).
    MathSciNet  PubMed  Article  Google Scholar 

    48.
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Article  Google Scholar 

    49.
    R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

    50.
    Stan Development Team. RStan: the R interface to Stan. R package v2.17.3 (2018).

    51.
    Rasmussen, C. et al. Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137, 297–306 (2018).
    CAS  Article  Google Scholar 

    52.
    Gelman, A. et al. Bayesian Data Analysis 3rd edn (Chapman and Hall, CRC, 2013).

    53.
    Howard, P. J. A. & Howard, D. M. Use of organic carbon and loss-on-ignition to estimate soil organic matter in different soil types and horizons. Biol. Fertil. Soils 9, 306–310 (1990).
    CAS  Article  Google Scholar  More

  • in

    Using hyperspectral imagery to investigate large-scale seagrass cover and genus distribution in a temperate coast

    1.
    York, P. H., Hyndes, G. A., Bishop, M. J. & Barnes, R. S. Faunal assemblages of seagrass ecosystems. In Seagrasses of Australia. Structure, Ecology and Conservation (eds A. W. D. Larkum, G. A. Kendrick, & P. J. Ralph) Ch. 17, 541–588 (Springer, Berlin, 2018).
    2.
    Nordlund, L. M., Koch, E. W., Barbier, E. B. & Creed, J. C. Seagrass ecosystem services and their variability across genera and geographical regions. PLoS ONE 11, e0163091 (2016).
    Article  Google Scholar 

    3.
    Camp, E. F. et al. Mangrove and seagrass beds provide different biogeochemical services for corals threatened by climate change. Front. Mar. Sci. 3, 52 (2016).
    Article  Google Scholar 

    4.
    Gaylard, S. G., Waycott, M. & Lavery, P. S. Review of coast and marine ecosystems in temperate Australia demonstrate a wealth of ecosystem services. Front. Mar. Sci. 7, 453 (2020).
    Article  Google Scholar 

    5.
    Burkholder, J. M., Tomasko, D. A. & Touchette, B. W. Seagrasses and eutrophication. J. Exp. Mar. Biol. Ecol. 350, 46–72 (2007).
    Article  Google Scholar 

    6.
    Kendrick, G. A. et al. Demographic and genetic connectivity: the role and consequences of reproduction, dispersal and recruitment in seagrasses. Biol. Rev. 92, 921–938 (2017).
    Article  Google Scholar 

    7.
    Kendrick, G. A. et al. Australian seagrass seascapes: present understanding and future research directions. In Seagrasses of Australia. Structure, Ecology and Conservation (eds Anthony W.D. Larkum, Gary A. Kendrick, & Peter J. Ralph) Ch. 9, 257–286 (Springer, Berlin, 2018).

    8.
    Hossain, M. S., Bujang, J. S., Zakaria, M. H. & Hashim, M. The application of remote sensing to seagrass ecosystems: an overview and future research prospects. Int. J. Remote Sens. 36, 61–114 (2015).
    Article  ADS  Google Scholar 

    9.
    Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl. Acad. Sci. USA 106, 12377–12381 (2009).
    CAS  Article  ADS  Google Scholar 

    10.
    Lefcheck, J. S. et al. Long-term nutrient reductions lead to the unprecedented recovery of a temperate coastal region. Proc. Natl. Acad. Sci. USA 115, 3658–3662 (2018).
    Article  ADS  Google Scholar 

    11.
    Tomasko, D. et al. Widespread recovery of seagrass coverage in Southwest Florida (USA): temporal and spatial trends and management actions responsible for success. Mar. Pollut. Bull. 135, 1128–1137 (2018).
    CAS  Article  Google Scholar 

    12.
    Carmen, B. et al. Recent trend reversal for declining European seagrass meadows. Nat. Commun. 10, 3356 (2019).
    Article  ADS  Google Scholar 

    13.
    Reise, K. & Kohlus, J. Seagrass recovery in the northern Wadden Sea?. Helgol. Mar. Res. 62, 77 (2008).
    Article  ADS  Google Scholar 

    14.
    Kendrick, G. A., Holmes, K. W. & Niel, K. P. V. Multi-scale spatial patterns of three seagrass species with different growth dynamics. Ecography 31, 191–200 (2008).
    Article  Google Scholar 

    15.
    Kirkman, H. Community structure in seagrasses in southern Western Australia. Aquat. Bot. 21, 363–375 (1985).
    Article  Google Scholar 

    16.
    Rasheed, M. A. Recovery and succession in a multi-species tropical seagrass meadow following experimental disturbance: the role of sexual and asexual reproduction. J. Exp. Mar. Biol. Ecol. 310, 13–45 (2004).
    Article  Google Scholar 

    17.
    Fearns, P. R. C., Klonowski, W., Babcock, R. C., England, P. & Phillips, J. Shallow water substrate mapping using hyperspectral remote sensing. Cont. Shelf Res. 31, 1249–1259 (2011).
    Article  ADS  Google Scholar 

    18.
    Kilminster, K. et al. Unravelling complexity in seagrass systems for management: Australia as a microcosm. Sci. Total Environ. 534, 97–109 (2015).
    CAS  Article  ADS  Google Scholar 

    19.
    Tanner, J. E. et al. Seagrass rehabilitation off metropolitan Adelaide: a case study of loss, action, failure, and success. Ecol. Manage. Restor. 15, 168–179 (2014).
    Article  Google Scholar 

    20.
    Sherman, C. D. et al. Reproductive, dispersal and recruitment strategies in australian seagrasses. In Seagrasses of Australia. Structure, Ecology and Conservation (eds Anthony W.D. Larkum, Gary A. Kendrick, & Peter J. Ralph) Ch. 8, 213–256 (Springer, Berlin, 2018).

    21.
    Hart, D. Near-shore seagrass change between 1949 and 1996 mapped using digital aerial orthophotography, metropolitan Adelaide area, Largs Bay-Aldinga, South Australia. A report prepared for the South Australian EPA. 12 (Department of Environment and Natural Resources, Adelaide, South Australia, 1997).

    22.
    Cameron, J. Near-shore seagrass change between 1995/6 and 2002 mapped using digital aerial orthophotography, metropolitan Adelaide area, North Haven-Sellicks Beach, South Australia. 21 (South Australian Department for Environment and Heritage, Adelaide, South Australia, 2003).

    23.
    Cameron, J. Near-shore seagrass change between 2002 and 2007 mapped using digital aerial orthophotography, metropolitan Adelaide area, Port Gawler-Marino, South Australia. 27 (Environment Protection Authority and Department for Environment and Heritage, Adelaide, South Australia, 2008).

    24.
    Hart, D. Seagrass extent change 2007–13 – Adelaide coastal waters. DEWNR technical note 2013/07. 19 (Department of Environment Water and Natural Resources, Adelaide, South Australia, 2013).

    25.
    Pu, R., Bell, S., Baggett, L., Meyer, C. & Zhao, Y. Discrimination of seagrass species and cover classes with in situ hyperspectral data. J. Coast. Res. 28, 1330–1344 (2012).
    Article  Google Scholar 

    26.
    Hedley, J. D., Harborne, A. R. & Mumby, P. J. Simple and robust removal of sun glint for mapping shallow-water benthos. Int. J. Remote Sens. 26, 2107–2112 (2005).
    Article  ADS  Google Scholar 

    27.
    Blackburn, D. T. & Dekker, A. G. Remote sensing study of marine and coastal features and interpretation of changes in relation to natural and anthropogenic processes. Final Technical Report. ACWS Technical Report No.6 prepared for the Adelaide Coastal Waters Study Steering Committee. 177 (David Blackburn Environmental Pty Ltd and CSIRO Land and Water, Adelaide, South Australia, 2006).

    28.
    Bryars, S. & Rowling, K. Benthic habitats of Eastern Gulf St Vincent: major changes in benthic cover and composition following European settlement of Adelaide. Trans. R. Soc S. Aust. 133, 318–338 (2009).
    Google Scholar 

    29.
    Kuo, J., Cambridge, M. L. & Kirkman, H. Anatomy and structure of australian seagrasses. In Seagrasses of Australia. Structure, Ecology and Conservation (eds Anthony W.D. Larkum, Gary A. Kendrick, & Peter J. Ralph) Ch. 4, 93–125 (Springer, Berlin, 2018).

    30.
    Neverauskas, V. P. Monitoring seagrass beds around a sewage sludge outfall in South Australia. Mar. Pollut. Bull. 18, 158–164 (1987).
    CAS  Article  Google Scholar 

    31.
    Bryars, S., Collings, G. & Miller, D. Nutrient exposure causes epiphytic changes and coincident declines in two temperate Australian seagrasses. Mar. Ecol. Prog. Ser. 441, 89–103 (2011).
    CAS  Article  ADS  Google Scholar 

    32.
    Neverauskas, V. P. Accumulation of periphyton biomass on artificial substrates deployed near a sewage sludge outfall in South Australia. Est. Coast. Shelf Sci. 25, 509–517 (1987).
    Article  ADS  Google Scholar 

    33.
    Burnell, O. W., Connell, S. D., Irving, A. D. & Russell, B. D. Asymmetric patterns of recovery in two habitat forming seagrass species following simulated overgrazing by urchins. J. Exp. Mar. Biol. Ecol. 448, 114–120 (2013).
    Article  Google Scholar 

    34.
    Westphalen, G. et al. A review of seagrass loss on the Adelaide metropolitan coastline. Adelaide Coastal Waters Study Technical Report No. 2, August 2004. SARDI Aquatic Sciences Publication No. RD04/0073. 68 (South Australian Research & Development Institute, Adelaide, South Australia, 2004).

    35.
    Bryars, S. & Neverauskas, V. Natural recolonisation of seagrasses at a disused sewage sludge outfall. Aquat. Bot. 80, 283–289 (2004).
    Article  Google Scholar 

    36.
    McDowell, L.-M. & Pfennig, P. Adelaide Coastal Water Quality Improvement Plan (ACWQIP) 162 (Adelaide, South Australia, 2013).
    Google Scholar 

    37.
    Cheshire, A. C. Adelaide Coastal Waters fore-sighting workshop report. Prepared for SA Environment Protection Authority. 102 (Science to Manage Uncertainty, Adelaide, South Australia, 2018).

    38.
    Wilkinson, J. et al. Volumes of inputs, their concentrations and loads received by Adelaide metropolitan coastal waters. ACWS Technical Report No. 18 prepared for the Adelaide Coastal Waters Study Steering Committee. 83 (Flinders Centre for Coastal and Catchment Environments (Flinders University of SA), Adelaide, South Australia, 2005).

    39.
    Pattiaratchi, C., Newgard, J. & Hollings, B. Physical oceanographic studies of Adelaide coastal waters using high resolution modelling, in-situ observations and satellite techniques – Sub Task 2 Final Technical Report. ACWS Technical Report No. 20 prepared for the Adelaide Coastal Waters Study Steering Committee. 92 (School of Environmental Systems Engineering (The University of Western Australia), Crawley, Australia, 2007).

    40.
    van Gils, J., Erftemeijer, P. L. A., Fernandes, M. & Daly, R. Development of the Adelaide Receiving Environment Model. Deltares report 1210877–000. 152 (Delft, the Netherlands, 2017).

    41.
    Gaylard, S., Nelson, M. & Noble, W. The South Australian monitoring, evaluation and reporting program for aquatic ecosystems: Rationale and methods for the assessment of nearshore marine waters. 70 (Environment Protection Authority, Adelaide, South Australia, 2013).

    42.
    Richter, R. & Schläpfer, D. Atmospheric/Topographic Correction for Airborne Imagery, ATCOR-4 User Guide, Version 4.2. 125 (DLR, Wessling, Germany, 2007).

    43.
    Whiteway, T. Australian Bathymetry and Topography Grid. Record 2009/21. (Geoscience Australia, Canberra, 2009). More

  • in

    Fossil evidence for vampire squid inhabiting oxygen-depleted ocean zones since at least the Oligocene

    1.
    Jenkyns, H. C. Geochemistry of oceanic anoxic events. Geochem. Geophy. Geosy. 11, Q03004 (2010).
    Google Scholar 
    2.
    Gambacorta, G., Bersezio, R., Weissert, H. & Erba, E. Onset and demise of Cretaceous oceanic anoxic events: The coupling of surface and bottom oceanic processes in two pelagic basins of the western Tethys. Paleoceanography 31, 732–757 (2016).
    Google Scholar 

    3.
    Palfy, J. & Smith, P. L. Synchrony between Early Jurassic extinction, oceanic anoxic event, and the Karoo–Ferrar flood basalt volcanism. Geology 28, 747–750 (2000).
    Google Scholar 

    4.
    Leckie, R. M., Bralower, T. J. & Cashman, R. Oceanic anoxic events and plankton evolution: Biotic response to tectonic forcing during the mid‐Cretaceous. Paleoceanography 17, 13-11–13-29 (2002).
    Google Scholar 

    5.
    Erba, E. Calcareous nannofossils and Mesozoic oceanic anoxic events. Mar. Micropaleontol. 52, 85–106 (2004).
    Google Scholar 

    6.
    Erbacher, J. V. J. T. & Thurow, J. Influence of oceanic anoxic events on the evolution of mid-Cretaceous radiolaria in the North Atlantic and western Tethys. Mar. Micropaleontol. 30, 139–158 (1997).
    Google Scholar 

    7.
    Harries, P. J. & Little, C. T. The early Toarcian (Early Jurassic) and the Cenomanian–Turonian (Late Cretaceous) mass extinctions: similarities and contrasts. Palaeogeogr. Palaeoclimatol. Palaeoecol. 154, 39–66 (1999).
    Google Scholar 

    8.
    Danise, S., Twitchett, R. J. & Little, C. T. Environmental controls on Jurassic marine ecosystems during global warming. Geology 43, 263–266 (2015).
    CAS  Google Scholar 

    9.
    Dera, G., Toumoulin, A. & De Baets, K. Diversity and morphological evolution of Jurassic belemnites from South Germany. Palaeogeogr. Palaeoclimatol. Palaeoecol. 457, 80–97 (2016).
    Google Scholar 

    10.
    Rita, P., Nätscher, P., Duarte, L. V., Weis, R. & De Baets, K. Mechanisms and drivers of belemnite body-size dynamics across the Pliensbachian–Toarcian crisis. Roy. Soc. Open Sci. 6, 190494 (2019).
    Google Scholar 

    11.
    Chun, C. Aus den Tiefen des Weltmeeres, 88 (ed. Fischer, G.) (Schilderungen von der Deutschen Tiefsee-Expedition, 1903).

    12.
    Seibel, B. A. et al. Vampire blood: respiratory physiology of the vampire squid (Vampyromorpha: Cephalopoda) in relation to the oxygen minimum layer. Exp. Biol. Online 4, 1–10 (1999).
    Google Scholar 

    13.
    Hoving, H. J. T. & Robison, B. H. Vampire squid: Detritivores in the oxygen minimum zone. Proc. Biol. Sci. 279, 4559–4567 (2012).
    PubMed  PubMed Central  Google Scholar 

    14.
    Golikov, A. V. et al. The first global deep-sea stable isotope assessment reveals the unique trophic ecology of Vampire Squid Vampyroteuthis infernalis (Cephalopoda). Sci. Rep. 9, 19099 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    15.
    Young, R. & Vecchione, M. Analysis of morphology to determine primary sister taxon relationships within coleoid cephalopods. Am. Malacol. Bull. 12, 91–112 (1996).
    Google Scholar 

    16.
    Strugnell, J. et al. Whole mitochondrial genome of the Ram’s Horn Squid shines light on the phylogenetic position of the monotypic order Spirulida (Haeckel, 1896). Mol. Phylogenet. Evol. 109, 296–301 (2017).
    CAS  Google Scholar 

    17.
    Sanchez, G. et al. Genus-level phylogeny of cephalopods using molecular markers: current status and problematic areas. PeerJ 6, e4331 (2018).
    PubMed  PubMed Central  Google Scholar 

    18.
    Lindgren, A. R. et al. A multi-gene phylogeny of Cephalopoda supports convergent morphological evolution in association with multiple habitat shifts in the marine environment. BMC Evol. Biol. 12, 129 (2012).
    PubMed  PubMed Central  Google Scholar 

    19.
    Tanner, A. R. et al. Molecular clocks indicate turnover and diversification of modern coleoid cephalopods during the Mesozoic marine revolution. Proc. Biol. Sci. 284, 20162818 (2017).
    PubMed  PubMed Central  Google Scholar 

    20.
    Lindgren, A. R., Giribet, G. & Nishiguchi, M. K. A combined approach to the phylogeny of Cephalopoda (Mollusca). Cladistics 20, 454–486 (2004).
    Google Scholar 

    21.
    Fara, E. What are Lazarus taxa? Geol. J. 36, 291–303 (2001).
    Google Scholar 

    22.
    Packard, A. Cephalopods and fish: the limits of convergence. Biol. Rev. 47, 241–307 (1972).
    CAS  Google Scholar 

    23.
    Nixon, M. & Young, J. Z. The Brains and Lives of Cephalopods, 1–406 (Oxford University Press, 2003).

    24.
    Kröger, B. et al. Cephalopod origin and evolution. Bioessays 33, 602–613 (2011).
    Google Scholar 

    25.
    Fuchs, D. Part M, Chapter 9B: the gladius and gladius vestige in fossil Coleoidea. Treatise Online 83, 1–23 (2016).
    Google Scholar 

    26.
    Fuchs, D. et al. The Muensterelloidea: phylogeny and character evolution of Mesozoic stem octopods. Pap. Palaeontol. 6, 31–92 (2019).
    Google Scholar 

    27.
    Fuchs, D. et al. The locomotion system of fossil Coleoidea (Cephalopoda) and its phylogenetic significance. Lethaia 49, 433–454 (2016).
    Google Scholar 

    28.
    Kretzoi, M. Necroteuthis n.gen. (Ceph. Dibr. Necroteuthidae n.f.) aus dem Oligozän von Budapest und das System der Dibranchiata. F.öldt. K.özl. (Bp.) 72, 124–138 (1942).
    Google Scholar 

    29.
    Donovan, D. T. Evolution of the dibranchiate Cephalopoda. Symp. Zool. Soc. Lond. 38, 15–48 (1977).
    Google Scholar 

    30.
    Riegraf, W., Janssen, N., & Schmitt-Riegraf, C. A. in Fossilum Catalogus I. Animalia, Vol. 135 (ed. Westphal, F.), 1–512 (1998).

    31.
    Fuchs, D. Part M, Coleoidea, chapter 23G: systematic descriptions: octobrachia. Treatise Online 138, 1–52 (2020).
    Google Scholar 

    32.
    Schulz, H. M., Bechtel, A. & Sachsenhofer, R. F. The birth of the Paratethys during the Early Oligocene: from Tethys to an ancient Black Sea analogue? Glob. Planet. Change 49, 163–176 (2005).
    Google Scholar 

    33.
    Bojanowski, M. J. et al. The Central Paratethys during Oligocene as an ancient counterpart of the present-day Black Sea: Unique records from the coccolith limestones. Mar. Geol. 403, 301–328 (2018).
    CAS  Google Scholar 

    34.
    Bizikov, V. A. Evolution of the shell in Cephalopoda, 1–448 (VNIRO, 2008).

    35.
    Weaver, P. G. et al. Characterization of organics consistent with β-Chitin preserved in the Late Eocene cuttlefish Mississaepia mississippiensis. PLoS ONE 6, e28195 (2011).
    CAS  PubMed  PubMed Central  Google Scholar 

    36.
    Kaiho, K. Benthic foraminiferal dissolved-oxygen index and dissolved-oxygen levels in the modern ocean. Geology 22, 719–722 (1994).
    CAS  Google Scholar 

    37.
    Bechtel, A. et al. Facies evolution and stratigraphic correlation in the early Oligocene Tard clay of Hungary as revealed by maceral, biomarker and stable isotope composition. Mar. Petrol. Geol. 35, 55–74 (2012).
    CAS  Google Scholar 

    38.
    Donovan, D. T. Part M., Chapter 9C: composition and structure of gladii in fossil Coleoidea. Treatise Online 75, 1–5 (2016).
    Google Scholar 

    39.
    Nagymarosy, A. et al. The effect of the relative sea-level changes in the north Hungarian Paleogene Basin. Geol. Soc. Greece Spec. Publ. 4, 247–253 (1995).
    Google Scholar 

    40.
    Ozsvárt, P. et al. The Eocene-Oligocene climate transition in the Central Paratethys. Palaeogeogr. Palaeoclimatol. Palaeoecol. 459, 471–487 (2016).
    Google Scholar 

    41.
    Nyerges, A., Kocsis, T. Á. & Pálfy, J. Changes in calcareous nannoplankton assemblages around the Eocene-Oligocene climate transition in the Hungarian Palaeogene Basin (Central Paratethys). Hist. Biol. 1–14. https://doi.org/10.1080/08912963.2019.1705295 (2020).
    Article  Google Scholar 

    42.
    Ozsvárt, P. Middle and Late Eocene benthic foraminiferal fauna from the Hungarian Paleogene Basin: systematics and paleoecology. Geol. Pannonica Spec. Pap. 2, 1–129 (2007).
    Google Scholar 

    43.
    Nagymarosy, A. Lower Oligocene nannoplankton in anoxic deposits of the central Paratethys. 8th International Nannoplankton Assoc. Conf., Bremen. J. Nannoplankton Res. 22, 128–129 (2000).
    Google Scholar 

    44.
    Nagymarosy, A. & Voronina, A. A. Calcareous nannoplankton from the Lower Maikopian beds (Early Oligocene, Union of Independent States). In Proc. 4thINA Conf. Prague 1991, Knihovnička ZPN 14b (eds Hamršmíd, B. & Young, J.) 187–221 (Nannoplankton Research, 1992).

    45.
    Murray, J. W. Ecology and Applications of Benthic Foraminifera, 1–426 (Cambridge University Press, 2006).

    46.
    Mørk, A. & Bromley, R. G. Ichnology of a marine regressive systems tract: the Middle Triassic of Svalbard. Polar Res. 27, 339–359 (2008).
    Google Scholar 

    47.
    Báldi, T. Mid-Tertiary Stratigraphy and Paleogeographic Evolution of Hungary, 1–201 (Akadémiai Kiadó, 1986).

    48.
    Khromov, D. N. Distribution patterns in Sepiidae. Smithson. Contr. Zool. 568, 191–206 (1998).
    Google Scholar 

    49.
    Sepkoski, J. J. Jr. A model of onshore-offshore change in faunal diversity. Paleobiology 17, 68–77 (1991).
    Google Scholar 

    50.
    Smith, A. B. & Stockley, B. The geological history of deep-sea colonization by echinoids: roles of surface productivity and deep-water ventilation. P. Roy. Soc. B Biol. Sci. 272, 865–869 (2005).
    Google Scholar 

    51.
    Thuy, B. et al. First glimpse into Lower Jurassic deep-sea biodiversity: in situ diversification and resilience against extinction. P. Roy. Soc. B Biol. Sci. 281, 20132624 (2014).
    Google Scholar 

    52.
    Jacobs, D. K. & Lindberg, D. R. Oxygen and evolutionary patterns in the sea: onshore/offshore trends and recent recruitment of deep-sea faunas. Proc. Natl Acad. Sci. USA 95, 9396–9401 (1998).
    CAS  Google Scholar 

    53.
    Zeidberg, L. D. & Robison, B. H. Invasive range expansion by the Humboldt squid, Dosidicus gigas, in the eastern North Pacific. Proc. Natl Acad. Sci. USA 104, 12948–12950 (2007).
    CAS  Google Scholar 

    54.
    Rogers, A. D. The role of the oceanic oxygen minima in generating biodiversity in the deep sea. Deep Sea Res. Pt. II 47, 119–148 (2000).
    Google Scholar 

    55.
    Levin, L. A. Oxygen minimum zone benthos: adaptation and community response to hypoxia. Oceanogr. Mar. Biol. Annu. Rev. 41, 1–45 (2003).
    Google Scholar 

    56.
    Childress, J. J. & Seibel, B. A. Life at stable low oxygen levels: adaptations of animals to oceanic oxygen minimum layers. J. Exp. Biol. 201, 1223–1232 (1998).
    CAS  Google Scholar 

    57.
    Gooday, A. J. et al. Habitat heterogeneity and its influence on benthic biodiversity in oxygen minimum zones. Mar. Ecol. 31, 125–147 (2010).
    Google Scholar 

    58.
    Wood, R. & Erwin, D. H. Innovation not recovery: dynamic redox promotes metazoan radiations. Biol. Rev. 93, 863–873 (2018).
    Google Scholar 

    59.
    Hermoso, M., Minoletti, F. & Pellenard, P. Black shale deposition during Toarcian super‐greenhouse driven by sea level. Clim 9, 2703–2712 (2013).
    Google Scholar 

    60.
    Kruta, I. et al. Proteroctopus ribeti in coleoid evolution. Paleontology 59, 767–773 (2016).
    Google Scholar 

    61.
    Wilby, P. R., Briggs, D. E. & Riou, B. Mineralization of soft-bodied invertebrates in a Jurassic metalliferous deposit. Geology 24, 847–850 (1996).
    CAS  Google Scholar 

    62.
    Etter, W. in Exceptional fossil preservation. A Unique View on the Evolution of Marine Life (eds Bottjer, D. J., Etter, W., Hagadorn, J. W. & Tang, C. M.) 293–305 (Columbia University Press, 2002).

    63.
    Charbonnier, S., Vannier, J., Gaillard, C., Bourseau, J.-P. & Hantzpergue, P. The La Voulte Lagerstätte (Callovian): Evidence for a deep water setting from sponge and crinoid communities. Palaeogeogr. Palaeoclimatol. Palaeoecol. 250, 216–236 (2007).
    Google Scholar 

    64.
    Charbonnier, S., Audo, D., Caze, B. & Biot, V. The La Voulte-sur-Rhône Lagerstätte (Middle Jurassic, France). CR Palevol 13, 369–381 (2014).
    Google Scholar 

    65.
    Vannier, J., Schoenemann, B., Gillot, B., S. Charbonnier, S. & Clarkson, E. Exceptional preservation of eye structure in arthropod visual predators from the Middle Jurassic. Nat. Commun. 7, 10320 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    66.
    Audo, D. et al. palaeoecology of Voulteryon parvulus (eucrustacea, polychelida) from the Middle Jurassic of La Voulte-sur-Rhône Fossil-Lagerstätte (France). Sci. Rep. 9, 1–13 (2019).
    CAS  Google Scholar 

    67.
    Viohl, G. in Solnhofen. Ein Fenster in die Jurazeit. (eds Arratia, G., Schultze, H.-P., Tischlinger, H. & Viohl, G.) 56–62 (Verlag Dr. Friedrich Pfeil, 2015).

    68.
    Engeser, T. & Reitner, J. Teuthiden aus dem Unterapt (“Töck”) von Helgoland (Schleswig-Holstein, Norddeutschland). Pal. Z. 59, 245–260 (1985).
    Google Scholar 

    69.
    Mutterlose, J., Pauly, S. & Steuber, T. Temperature controlled deposition of early Cretaceous (Barremian–early Aptian) black shales in an epicontinental sea. Palaeogeogr. Palaeoclimatol. Palaeoecol. 273, 330–345 (2009).
    Google Scholar 

    70.
    Heldt, M., Mutterlose, J., Berner, U. & Erbacher, J. First high-resolution δ13C-records across black shales of the Early Aptian Oceanic Anoxic Event 1a within the mid-latitudes of northwest Europe (Germany, Lower Saxony Basin). Newsl. Stratigr. 45, 151–169 (2012).
    Google Scholar 

    71.
    Bottini, C. & Mutterlose, J. Integrated stratigraphy of Early Aptian black shalesin the Boreal Realm: calcareous nanofossil and stable isotope evidence forglobal and regional processes. Newsl. Stratigr. 45, 115–137 (2012).
    Google Scholar 

    72.
    Landman, N. H. et al. Ammonite extinction and nautilid survival at the end of the Cretaceous. Geology 42, 707–710 (2014).
    CAS  Google Scholar 

    73.
    Fuchs, D., Laptikhovsky, V., Nikolaeva, S., Ippolitov, A. & Rogov, M. Evolution of reproductive strategies in coleoid mollusks. Paleobiology 46, 82–103 (2020).
    Google Scholar 

    74.
    Tajika, A., Nützel, A. & Klug, C. The old and the new plankton: ecological replacement of associations of mollusc plankton and giant filter feeders after the Cretaceous? PeerJ 6, e4219 (2018).
    PubMed  PubMed Central  Google Scholar 

    75.
    Lu, C. C. & Clarke, M. R. Vertical distribution of cephalopods at 40°N, 53°N and 60°N at 20°W in the North Atlantic. J. Mar. Biol. Assoc. U.K. 55, 143–163 (1975).
    Google Scholar 

    76.
    Clements, T., Colleary, C., De Baets, K. & Vinther, J. Buoyancy mechanisms limit preservation of coleoid cephalopod soft tissues in Mesozoic Lagerstätten. Palaeontology 60, 1–14 (2017).
    Google Scholar 

    77.
    Košťák, M., Kohout, O., Mazuch, M. & Čech, S. An unusual occurrence of vascoceratid ammonites in the Bohemian Cretaceous Basin (Czech Republic) marks the lower Turonian boundary between the Boreal and Tethyan realms in central Europe. Cret. Res. 108, 104338 (2020).
    Google Scholar 

    78.
    Oji, T. in Palaeobiology II (eds Briggs, D. E. G. & Crowther, P. R.) 444–447 (Blackwell Science Ltd, 2001).

    79.
    Báldi, T. A. in Geológiai Kirándulások Magyarország Közepén (ed. Palotai, M.) 94–129 (Hantken Kiadó, 2010).

    80.
    Tari, G. et al. Paleogene retroarc flexural basin beneath the Neogene Pannonian Basin: a geodynamic model. Tectonophysics 226, 433–455 (1993).
    Google Scholar 

    81.
    Švábenická, L. et al. Biostratigraphy and paleoenvironmental changes on the transition from the Menilite to Krosno lithofacies (Western Carpathians, Czech Republic). Geol. Carpath. 58, 237–262 (2007).
    Google Scholar 

    82.
    Kováč, M. et al. Paleogene palaeogeography and basin evolution of the Western Carpathians, Northern Pannonian domain and adjoining areas. Glob. Planet. Change 140, 9–27 (2016).
    Google Scholar 

    83.
    Nevesskaja, L. A. et al. History of Paratethys. Ann. Inst. Géol. Hong. 70, 337–342 (1987).
    Google Scholar 

    84.
    Lafuente, B., Downs, R. T., Yang, H. & Stone, N. in Highlights in Mineralogical Crystallography (eds Armbruster, T. & Danisi, R. M.) 1–30 (De Gruyter, 2015).

    85.
    McCrea, J. M. On the isotopic chemistry of carbonates and a paleotemperature scale. J. Chem. Phys. 18, 849–857 (1950).
    CAS  Google Scholar 

    86.
    Guiry, M. D. & Guiry, G. M. AlgaeBase (World-wide electronic publication, National University of Ireland, Galway, accessed May 18, 2020); https://www.algaebase.org.

    87.
    Holcová, K. Postmortem transport and resedimentation of foraminiferal tests: relations to cyclical changes of foraminiferal assemblages. Palaeogeogr. Palaeoclimatol. Palaeoecol. 145, 157–182 (1999).
    Google Scholar 

    88.
    Folk, R. L. Nannobacteria and the formation of framboidal pyrite: Textural evidence. J. Earth Syst. Sci. 114, 369–374 (2005).
    Google Scholar 

    89.
    Zágoršek, K. et al. Bryozoan event from Middle Miocene (Early Badenian) lower neritic sediments from the locality Kralice nad Oslavou (Central Paratethys, Moravian part of the Carpathian Foredeep). Int. J. Earth. Sci. 97, 835–850 (2007).

    90.
    Košťák, M. et al. Micro-computed tomography data supporting the manuscript: Fossil evidence for vampire squid inhabiting oxygen-depleted ocean zones since at least the Oligocene. figshare https://doi.org/10.6084/m9.figshare.13526024 (2021). More