More stories

  • in

    Gene-drive suppression of mosquito populations in large cages as a bridge between lab and field

    Study designInitially, we assessed life-history traits of both Ag(QFS1) males and females as well as of the wild-type strain G3 of An. gambiae and assessed their longevity under large-cage conditions (4.7 m3) in order to emulate more natural population dynamics16 (see Fig. 2, Supplementary Material). Considering the initial Kaplan–Meier Survival estimate of wild-type G3 adult mosquitoes in 4.7 m3 cages of 2 m × 1 m × 2.35 m size and the establishment of overlapping generations with bi-weekly introductions of 400 G3 pupae with a start-up population of 800 mosquitoes, we then analysed ASL populations with an expected mean size of ~570 adult mosquitoes as ‘receiving’ populations for gene drive release experiments (Source Data). To mimic field-like conditions absent in small cage conditions, the climate chambers were maintained under near-natural environmental conditions including simulated dusk, dawn and daylight, and each cage was equipped with proven swarming stimuli and a resting shelter14 (Fig. 1). Under these conditions male swarming, an important component of successful mating behaviour, was frequently observed. To mimic a hypothetical field gene drive release, we seeded Ag(QFS1) mosquitoes over a single week (two releases) into the established ‘receiving’ wild-type populations at two different starting frequencies, low (12.5% initial allele frequency) and medium (25% allele frequency), as well as control cages (0% gene drive release), all in duplicate (6 cages total). The ASL population dynamics and the potential selection of drive-resistant alleles were monitored in treated and control cages until wild-type populations were fully suppressed by the gene drive in the treatments. Finally, we constructed an individual-based stochastic simulation model of the experiment to better understand the observed dynamics of the gene drive frequency and population suppression.Mosquito strainsTwo An. gambiae mosquito strains were used, the wild-type G3 strain (MRA-112) and Female Sterile Gene Drive strain, Ag(QFS)1, previously known as dsxFCRISPRh9.This strain contains a Cas9-based homing cassette within the coding sequence of the female-specific exon 5 of the dsx gene (Supplementary Fig. 1). The cassette includes a human codon-optimised Streptococcus pyogenes Cas9 (hSpCas9)29 gene under the regulation of the zero population growth (zpg) promoter and terminator30 of An. gambiae and a gRNA against exon 5 under the control of the An. gambiae U6 snRNA promoter. The cassette also carries a dsRed fluorescent protein marker under the expression of the 3xP3 promoter.Mosquito containment and maintenanceAnopheles gambiae mosquito strains were contained in a purpose-built Arthropod Containment Level 2 plus facility at Polo d’Innovazione di Genomica, Genetica e Biologia, Genetics & Ecology Research Centre, Terni, Italy. Mosquitoes were reared in cubical cages of 17.5 cm × 17.5 cm × 17.5 cm (BugDorm-4) as described in Valerio et al.31 at 28 °C and 80% relative humidity (Supplementary Fig. 2). Larvae were maintained in trays (253 × 353 × 81 mm) at a density of 200 larvae per tray using 400 mL deionized water with sea salt at a concentration of 0.3 g/L and 5 mL of 2% w/v larval diet32 and screened for fluorescent markers en masse using a Complex Object Parametric Analyzer and Sorter (COPAS, Union Biometrica, Boston, USA).Large-cage environmentFor experimental purposes, mosquitoes were housed in a large-cage environment as described in Pollegioni et al.16 A single large climatic chamber was equipped with six 4.7 m3 cages of 2 m × 1 m × 2.35 m (length, width and height) (Fig. 1) and maintained at 28 °C ± 0.5 °C and 80 ± 5% relative humidity (Fig. 1, Supplementary Fig. 2). The climatic chamber was illuminated by three sets of three LEDs (3000, 4000 and 6500 K correlated colour temperatures) controlled by Winkratos software (ANGELANTONI Industries S.p.A, Massa Martana, Italy), allowing a gentle transition between light and dark sufficient to emulate dawn, and dusk. For the purpose of the current study, full light conditions (800 lux) were simulated using all LEDs and adjusted to last 11 h and 15 min. Cages were additionally equipped with ambient lighting (3000 K) designed to stimulate swarming14, and a terracotta resting shelter moistened with a soaked sponge. Mosquitoes were fed on 10% sucrose and 0.1% methylparaben solution and blood fed bi-weekly using defibrinated and heparinized sterile cow blood via the Hemotek membrane feeder (Discovery Workshops, Accrington, 34 UK). Oviposition sites consisted of a 12 cm diameter Petri dish with a wet filter paper strip introduced 2 days after the blood meal. Mosquito pupae, food, blood and water were introduced or removed through two openings, 12 cm in diameter, at the front of each cage with no operators entering the cage. Blood meal was administered by the introduction of two Hemotek feeders in each cage through one of the two openings at the front, leaving the power unit outside. No adult mosquitoes were removed from the large cages throughout the cage trials.Measuring the life-history parametersTo assess life-history parameters of wild-type G3 and Ag(QFS)1 strains, standardised phenotypic assays were performed as described in Pollegioni et al.16. In brief, clutch size, hatching rate, larval, pupal and adult mortality rates, as well as the bias in transgenics among the offspring of heterozygous Ag(QFS)1 were measured in wild-type G3 and Ag(QFS)1 strains in triplicate in standard small laboratory cages (BugDorm-4). Ag(QFS)1 heterozygotes used in these assays had inherited the drive allele paternally and were therefore subject to paternal, but not maternal, effects of embryonic nuclease deposition that can lead to a mosaicism of somatic mutations at the doublesex locus and a resultant effect on fitness9. 150 females and 150 males were mated to wild-type mosquitoes for 4 days, blood fed and their progeny counted as eggs using EggCounter v1.0 software33. Hatching rate was evaluated 3 days post oviposition by visually inspecting 200 eggs under a stereomicroscope (Stereo Microscope M60, Leica Microsystems, Germany). Sex-specific larval mortality was calculated by rearing 200 larvae/tray and counting/sexing the number of surviving pupae.Sex-specific adult survival was assessed in triplicate for each genotype separately by introducing and sexing 100 male and 100 female pupae of G3 and heterozygous Ag(QFS)1 into either small (0.0049 m³) or large cages (4.7 m³) (Supplementary Fig. 3). In the small cages, we tested 100 individuals in each cage divided by genotype and sex. In each large cage, 100 male and 100 female pupae following sexing and counting were tested together. Because homozygous Ag(QFS)1 do not show clear sex-specific phenotypes as pupae9, 100 Ag(QFS)1 total homozygotes (males and intersex females) were introduced into the small and large cages unsexed (Supplementary Fig. 3a). Sex-specific survival of emerged adults was calculated from daily collections of dead adult mosquitoes from the respective cages and their sexing. The adult survival assays in large cages were performed twice, one before the large-cage Ag(QFS)1 release experiment started and one after the large-cage Ag(QFS)1 release experiment finished. For the latter adult survival assay, around 400 individual mosquitoes were collected from large-cage populations at larval stage (before the cage populations declined, day 231 and 311 post-release for Ag(QFS)1 and G3 wild type, respectively), and kept in small cages until the start of the assay (Supplementary Fig. 3b).Establishment, maintenance and monitoring of age-structured large cage (ASL) populationsTo test the suppressive potential of Ag(QFS)1, we first established stable ASL populations of An. gambiae (G3 strain) housed in a purpose-built climatic chamber. Each population was initiated and maintained at the maximum rearing capacity through twice-weekly introductions of 400 G3 pupae (200 males and 200 females) over a period of 21 days (establishment), estimated to sustain a mean adult population of 574 mosquitoes based on the initial Kaplan–Meier estimate (Supplementary Fig. 3a). After this initial period only progeny of these populations were used to repopulate the cages twice-weekly (re-stocking) for a period of 53 days (pre-release, 74 days total), or supplemented with wild type reared separately when progeny numbers were too low. Each ASL population was considered stabilised after retrieving a sufficiently large and stable number of eggs to restock the population over four consecutive weeks. In detail, the receiving populations in all six cages were stabilised to produce a similar number of eggs in the 31 days before Ag(QFS)1 release, with an average egg production per cage ranging from 2262 to 5334. Twice-weekly blood meals were initiated at dusk and extended for a period of 5 h, and oviposition sites were illuminated with blue light for egg collection 2 days later. Eggs were removed from the cages, counted, and allowed to hatch in a single tray within the climatic test chamber. For re-stocking the cage populations with wild-type pupae, a maximum of 400 randomly selected pupae were collected at the peak of pupation, manually sexed and screened and introduced to their respective cage twice per week.Ag(QFS)1 release experiments in large cagesTo assess invasion dynamics of the Ag(QFS)1 strain in ASL populations of Anopheles gambiae, we performed duplicate releases designed to randomly seed ASL populations at low (12.5%, cages 2 and 5) or medium (25%, cages 3 and 6) allelic frequencies. After 74 days pre-release initiation period, heterozygous Ag(QFS)1 males were released into duplicate cages in addition to the regular re-stocking of the ASL populations with wild-type pupae. Releases took place on two consecutive re-stocking occasions, representing 15.2% (71 and 72) or 26.3% (142 and 143) of pupae introduced that week (943 and 1085, respectively), equivalent to 25 or 50% of the estimated mean pre-released adult population (on average 574 mosquitoes were present in large cages). No further releases were carried out and indoor ASL populations were maintained through re-stocking of 400 pupae twice per week. From then, the ASL populations were maintained in the same way we established the receiving population, with the same constant re-stocking rate from offspring. No adult mosquitoes were removed from the cages. Duplicate control cages were similarly maintained, but without release of Ag(QFS)1.While not statistically significant (Kruskal–Wallis Test P = 0.06 ns), there was some variation in reproductive output amongst the six cages due to random effects (cage 1: mean egg number = 4265.77, CI 95% = 1550.36; cage 2: mean egg number = 2691.73, CI 95% = 790.41; cage 3: mean egg number = 2517.46, CI 95% = 889.66; cage 4: mean egg number = 1799.18, CI 95% = 573.18; cage 5: mean egg number = 2350.82, CI 95% = 745.44; cage 6: mean egg number = 2060.05, CI 95% = 767.77). To control for random effects that could affect reproductive capacity of the population independently of the effect of the gene drive, we chose as control populations those cages with reproductive output at the upper and lower end of the distribution (cages 1 and 4). Replicate gene-drive release cages were distributed to cages 2 and 5 (12.5% allelic frequency) and cages 3 and 6 (25% allelic frequency) to mitigate against potential local environmental position effects (Fig. 2).Key indicators of population fitness and drive invasion were monitored for the duration of the experiment, including total egg output, hatching rate, pupal mortality, and the frequency of transgenics amongst L1 offspring and the pupal cohorts used for re-stocking. Total larvae were counted and screened for RFP fluorescence linked to Ag(QFS)1 using the COPAS larval sorter, and 1000 randomly selected to rear at a density of 200 per tray. Pupae positive for the gene drive element could be identified by expression of the RFP marker gene that is contained within the genetic element. Triplicate samples of up to 400 L1 larvae were stored in absolute ethanol at −80 °C for subsequent analysis.ModellingA stochastic model was set up to replicate the experimental design with respect to twice-weekly egg laying, the initiation phase, the transgene introductions, and the subsequent monitoring phase (Supplementary Methods). In brief, daily changes to the population result from egg laying, deaths, and matings, and are assumed to occur with probabilities that may be genotype specific. Adult longevity parameters were estimated from the large-cage survival assays that were performed before the gene-drive release experiments began, and after the gene-drive dynamics had run their course. The ASL caged populations showed a similar trend of increasing egg output over time prior to the suppressive effect of the drive (Fig. 2a–c) that may be explained by a general increase in adult survival that was observed between the start and end of the population experiment (Supplementary Fig. 3). To account for these changes in the stochastic model, we assumed a small increase in adult survival over time, irrespective of genotype, based on experimental data (Supplementary Fig. 3).We were particularly interested in the drive allele fertility costs, because these are potentially important to drive allele dynamics in natural populations22,23. Fertility costs may arise from paternal and maternal effects of Cas9 deposition into the sperm or egg, or from ectopic activity of Cas9 in the soma9. It is therefore possible that female offspring of transgenic fathers differ, in terms of fertility, from female offspring of transgenic mothers, and to investigate this possibility we fitted a separate parameter for the fertility of each type of female.We compared the data to model simulations using a suite of summary statistics34 (Supplementary Methods) to infer the fertility of females with a transgenic father or mother. In addition, we inferred two parameters that determined the egg production of unaffected (wild-type) females, and one parameter that determined the rate of R2 allele creation. We obtained a posterior distribution for all five parameters by retaining the 200 best fitting parameter combinations from 50,000 parameter samples generated by a Monte-Carlo algorithm (Supplementary Table 1). The simulation codes are available from Github: https://github.com/AceRNorth/TerniLargeCage.Pooled amplicon sequencing and analysisWe previously developed a strategy to detect and quantify target-site resistance based upon targeted amplicon sequencing using pooled samples of larvae6, and found no evidence for resistance to Ag(QFS)1 in small caged release populations9. To further investigate resistance in the large-caged release experiment, we analysed mutations found at the genomic target of Ag(QFS)1 in samples collected at early and late timepoints. Genomic DNA (gDNA) was extracted en masse from triplicate samples of 400 L1 larvae, or 50–300 larvae where larval numbers were limiting, that were collected after blood meals given on days 4 and 193 from all 6 cages, and on day 235 where sufficient larvae were available.gDNA extractions were performed using the DNeasy Blood & Tissue kit (Qiagen). 100 ng of extracted gDNA was used to amplify a 291 bp region spanning the target site of Ag(QFS)1 in doublesex, using the KAPA HiFi HotStart Ready Mix PCR kit (Kapa Biosystems) and primers containing Illumina Genewiz AmpEZ partial adaptors (underlined): Illumina-AmpEZ-4050-F1 ACACTCTTTCCCTACACGACGCTCTTCCGATCTACTTATCGGCATCAGTTGCG and Illumina-AmpEZ-4050-R1 GACTGGAGTTCAGACGTGTGCTCTTCCGATCTGTGAATTCCGTCAGCCAGC. PCR reactions were performed under non-saturating conditions and run for 25 cycles, as in Hammond et al.6 to maintain proportional representation of alleles from the extracted gDNA in the PCR products.Pooled amplicon sequencing reads, averaging ~1.5 million per condition, were analysed using CRISPResso235, using an average read quality threshold of 30. Insertions and deletions were included if they altered a window of 20 bp surrounding the cleavage site that was chosen on the basis of previously observed mutations at this locus9. Individual allele frequencies were calculated based upon their total frequency in triplicate samples. A threshold frequency of 0.25% per mutant allele was set to distinguish putative resistant alleles from sequencing error20.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

  • in

    The influence of subcolony-scale nesting habitat on the reproductive success of Adélie penguins

    1.Brown, C. R. The ecology and evolution of colony-size variation. Behav. Ecol. Sociobiol. 70, 1613–1632 (2016).Article 

    Google Scholar 
    2.Brown, C. R., Stutchbury, B. J. & Walsh, P. D. Choice of colony size in birds. Trends Ecol. Evol. 5, 398–403 (1990).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Wittenberger, J. F. & Hunt, G. L. The adaptive significance of coloniality in birds. Avian Biol. 8, 1–78 (1985).
    Google Scholar 
    4.Ainley, D. G., Nur, N. & Woehler, E. J. Factors affecting the distribution and size of Pygoscelid penguin colonies in the Antarctic. Auk 112, 171–182 (1995).Article 

    Google Scholar 
    5.Forero, M. G., Tella, J. L., Hobson, K. A., Bertellotti, M. & Blanco, G. Conspecific food competition explains variability in colony size: A test in Magellanic Penguins. Ecology 83, 3466–3475 (2002).Article 

    Google Scholar 
    6.Hunt, G. L., Eppley, Z. A. & Schneider, D. C. Reproductive performance of seabirds: The importance of population and colony size. Auk 103, 306–317 (1986).Article 

    Google Scholar 
    7.Brunton, D. ‘Optimal’ colony size for least terns: An inter-colony study of opposing selective pressures by predators. Condor 101, 607–615 (1999).Article 

    Google Scholar 
    8.Lyver, P. O. et al. Trends in the breeding population of Adélie penguins in the Ross Sea, 1981–2012: A coincidence of climate and resource extraction effects. PLoS ONE 9, e91188 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    9.Croxall, J. P. et al. Seabird conservation status, threats and priority actions: A global assessment. Bird Conserv. Int. 22, 1–34 (2012).Article 

    Google Scholar 
    10.Paleczny, M., Hammill, E., Karpouzi, V. & Pauly, D. Population trend of the world’s monitored seabirds, 1950–2010. PLoS ONE 10, e0129342 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    11.Hinke, J., Polito, M., Reiss, C., Trivelpiece, S. & Trivelpiece, W. Flexible reproductive timing can buffer reproductive success of Pygoscelis spp. penguins in the Antarctic Peninsula region. Mar. Ecol. Prog. Ser. 454, 91–104 (2012).ADS 
    Article 

    Google Scholar 
    12.Elliott, M. L. et al. Brandt’s cormorant diet (1994–2012) indicates the importance of fall ocean conditions for northern anchovy in central California. Fish. Oceanogr. 25, 515–528 (2016).Article 

    Google Scholar 
    13.Cairns, D. K. Population regulation of seabird colonies. In Current Ornithology (ed. Power, D. M.) 37–61 (Springer US, 1992).Chapter 

    Google Scholar 
    14.Aebischer, N. J., Coulson, J. C. & Colebrook, J. M. Parallel long-term trends across four marine trophic levels and weather. Nature 347, 753–755 (1990).ADS 
    Article 

    Google Scholar 
    15.Saether, B. E. & Bakke, O. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).Article 

    Google Scholar 
    16.Jenouvrier, S., Barbraud, C., Cazelles, B. & Weimerskirch, H. Modelling population dynamics of seabirds: Importance of the effects of climate fluctuations on breeding proportions. Oikos 108, 511–522 (2005).Article 

    Google Scholar 
    17.Schmidt, A. E. et al. Changing environmental spectra influence age-structured populations: Increasing ENSO frequency could diminish variance and extinction risk in long-lived seabirds. Theor. Ecol. 11, 367–377 (2018).Article 

    Google Scholar 
    18.Kokko, H., Harris, M. P. & Wanless, S. Competition for breeding sites and site-dependent, population regulation in a highly colonial seabird, the common guillemot Uria aalge. J. Anim. Ecol. 73, 367–376 (2004).Article 

    Google Scholar 
    19.Oro, D. Living in a ghetto within a local population: An empirical example of an ideal despotic distribution. Ecology 89, 838–846 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Stokes, D. L. & Boersma, P. D. Nest-site characteristics and reproductive success in Magellanic Penguins (Spheniscus magellanicus). Auk 115, 34–49 (1998).Article 

    Google Scholar 
    21.Velando, A. & Freire, J. Nest site characteristics, occupation, and breeding success in the European Shag. Waterbirds 26, 473 (2003).Article 

    Google Scholar 
    22.Coulson, J. C. Colonial breeding in seabirds. In Biology of Marine Birds (eds Schreiber, E. A. & Burger, J.) 87–113 (CRC Press, 2002).
    Google Scholar 
    23.Liljesthröm, M., Emslie, S. D., Frierson, D. & Schiavini, A. Avian predation at a Southern Rockhopper Penguin colony on Staten Island, Argentina. Polar Biol. 31, 465–474 (2007).Article 

    Google Scholar 
    24.Frere, E., Gandini, P. & Boersma, P. D. Effects of nest type on reproductive success of the Magellanic penguin Spenishcus magellanicus. Mar. Ornithol. 20, 1–6 (1992).
    Google Scholar 
    25.Emslie, S. D., Karnovsky, N. & Trivelpiece, W. Avian predation at penguin colonies on King George Island, Antarctica. Wilson Bull. 107, 317–327 (1995).
    Google Scholar 
    26.Gaston, A. J. & Elliot, R. D. Predation by Ravens Corvus corax on Brunnich’s Guillemot Uria lomvia eggs and chicks and its possible impact on breeding site selection. Ibis 138, 742–748 (1996).Article 

    Google Scholar 
    27.Taylor, R. H. The Adélie penguin Pygoscelis adeliae at Cape Royds. Ibis 104, 176–204 (1962).Article 

    Google Scholar 
    28.Votier, S. C., Heubeck, M. & Furness, R. W. Using inter-colony variation in demographic parameters to assess the impact of skua predation on seabird populations. Ibis 150, 45–53 (2008).Article 

    Google Scholar 
    29.Hamilton, W. D. Geometry for the selfish herd. J. Theor. Biol. 31, 295–311 (1971).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Weidinger, K. Effect of predation by skuas on breeding success of the Cape petrel Daption capense at Nelson Island, Antarctica. Polar Biol. 20, 170–177 (1998).Article 

    Google Scholar 
    31.Lynch, H. J. & LaRue, M. A. First global census of the Adélie Penguin. Auk 131, 457–466 (2014).Article 

    Google Scholar 
    32.Ainley, D. The Adélie Penguin: Bellwether of Climate Change (Columbia University Press, 2002).Book 

    Google Scholar 
    33.Borowicz, A. et al. Multi-modal survey of Adélie penguin mega-colonies reveals the Danger Islands as a seabird hotspot. Sci. Rep. 8, 3926 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    34.Bracegirdle, T. J., Connolley, W. M. & Turner, J. Antarctic climate change over the twenty first century. J. Geophys. Res. 113, D03103 (2008).ADS 

    Google Scholar 
    35.Smith, W. O., Ainley, D. G., Arrigo, K. R. & Dinniman, M. S. The oceanography and ecology of the Ross Sea. Ann. Rev. Mar. Sci. 6, 469–487 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Ainley, D. et al. Antarctic penguin response to habitat change as Earth’s troposphere reaches 2 C above pre industrial levels. Ecol. Monogr. 80, 49–66 (2010).Article 

    Google Scholar 
    37.Cimino, M. A., Lynch, H. J., Saba, V. S. & Oliver, M. J. Projected asymmetric response of Adélie penguins to Antarctic climate change. Sci. Rep. 6, 28785 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Fraser, W. R., Patterson-Fraser, D. L., Ribic, C. A., Schofield, O. & Ducklow, H. A nonmarine source of variability in Adélie penguin demography. Oceanography 26, 207–209 (2013).Article 

    Google Scholar 
    39.Cimino, M. A., Patterson-Fraser, D. L., Stammerjohn, S. & Fraser, W. R. The interaction between island geomorphology and environmental parameters drives Adélie penguin breeding phenology on neighboring islands near Palmer Station, Antarctica. Ecol. Evol. 9, 9334–9349 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Patterson, D. L., Easter-Pilcher, A. L. & Fraser, W. R. The effects of human activity and environmental variability on long-term changes in Adélie penguin populations at Palmer Station, Antarctica. In Antarctic Biology in a Global Context (eds. van der Vies, S. M. et al.) 301–307 (2003).
    Google Scholar 
    41.Bricher, P. K., Lucieer, A. & Woehler, E. J. Population trends of Adélie penguin (Pygoscelis adeliae) breeding colonies: A spatial analysis of the effects of snow accumulation and human activities. Polar Biol. 31, 1397–1407 (2008).Article 

    Google Scholar 
    42.Ainley, D. G., LeResche, R. E. & Sladen, W. J. L. Breeding Biology of the Adélie Penguin (1983).
    Google Scholar 
    43.Stonehouse, B. Observations on Adélie penguins (Pygoscelis adeliae) at Cape Royds, Antarctica. In Proc. XIIIth Internatl. Ornith. Congr. Vol. 1963, 766–779 (1963).44.Ainley, D. G. et al. Diet and foraging effort of Adélie penguins in relation to pack-ice conditions in the southern Ross Sea. Polar Biol. 20, 311–319 (1998).Article 

    Google Scholar 
    45.Ballard, G., Ainley, D. G., Ribic, C. A. & Barton, K. R. Effect of instrument attachment and other factors on foraging trip duration and nesting success of Adélie penguins. Condor 103, 481–490 (2001).Article 

    Google Scholar 
    46.Ainley, D. G. et al. Post-fledging survival of Adélie penguins at multiple colonies: Chicks raised on fish do well. Mar. Ecol. Prog. Ser. 601, 239–251 (2018).ADS 
    Article 

    Google Scholar 
    47.Dugger, K. M., Ballard, G., Ainley, D. G., Lyver, P. O. & Schine, C. Adélie penguins coping with environmental change: Results from a natural experiment at the edge of their breeding range. Front. Ecol. Evol. 2, 1–12 (2014).Article 

    Google Scholar 
    48.Ainley, D. G. et al. Decadal-scale changes in the climate and biota of the Pacific sector of the Southern Ocean, 1950s to the 1990s. Antarct. Sci. 17, 171–182 (2005).ADS 
    Article 

    Google Scholar 
    49.Lee, J. R. et al. Climate change drives expansion of Antarctic ice-free habitat. Nature 547, 49–54 (2017).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.LaRue, M. A. et al. Climate change winners: Receding ice fields facilitate colony expansion and altered dynamics in an Adélie penguin metapopulation. PLoS ONE 8, e60568 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Emslie, S. D., Berkman, P. A., Ainley, D. G., Coats, L. & Polito, M. Late-Holocene initiation of ice-free ecosystems in the southern Ross Sea, Antarctica. Mar. Ecol. Prog. Ser. 262, 19–25 (2003).ADS 
    Article 

    Google Scholar 
    52.Emslie, S. D., Coats, L. & Licht, K. A 45,000 yr record of Adélie penguins and climate change in the Ross Sea, Antarctica. Geology 35, 61–64 (2007).ADS 
    Article 

    Google Scholar 
    53.Penney, R. L. Territorial and social behavior in the Adélie Penguin. Antarct. Bird Stud. 12, 83–131 (1968).
    Google Scholar 
    54.LaRue, M. A. et al. A method for estimating colony sizes of Adélie penguins using remote sensing imagery. Polar Biol. 37, 507–517 (2014).Article 

    Google Scholar 
    55.De Neve, L., Fargallo, J. A., Polo, V., Martin, J. & Soler, M. Subcolony characteristics and breeding performance in the Chinstrap Penguin Pygoscelis antarctica. Ardeola 53, 19–29 (2006).
    Google Scholar 
    56.Winstral, A., Elder, K. & Davis, R. E. Spatial snow modeling of wind-redistributed snow using terrain-based parameters. J. Hdyrometeorol. 3, 524–538 (2002).ADS 
    Article 

    Google Scholar 
    57.Plattner, C. H., Braun, L. N. & Brenning, A. Spatial variability of snow accumulation on Vernagtferner, Austrian Alps, in winter 2003/04. Z. Gletscherkd. Glazialgeol. 39, 43–57 (2006).
    Google Scholar 
    58.Young, E. Skua and Penguin: Predator and Prey (Cambridge University Press, 1994).Book 

    Google Scholar 
    59.Trillmich, F. Feeding Territories and breeding success of South Polar Skuas. Auk 95, 23–33 (1978).Article 

    Google Scholar 
    60.Moret, G. J. M. & Huerta, A. D. Correcting GIS-based slope aspect calculations for the Polar Regions. Antarct. Sci. 19, 129–130 (2007).ADS 
    Article 

    Google Scholar 
    61.Seefeldt, M. W., Tripoli, G. J. & Stearns, C. R. A high-resolution numerical simulation of the wind flow in the Ross Island region, Antarctica. Mon. Weather Rev. 131, 435–458 (2003).ADS 
    Article 

    Google Scholar 
    62.Jammalamadaka, S. R., Rao Jammalamadaka, S. & SenGupta, A. Topics in circular statistics. Ser. Multivariate Anal. https://doi.org/10.1142/4031 (2001).Article 
    MATH 

    Google Scholar 
    63.Watson, G. S. Goodness-of-fit tests on a circle. II.. Biometrika 49, 57–63 (1962).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    64.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).MATH 
    Book 

    Google Scholar 
    65.Marra, G. & Wood, S. N. Practical variable selection for generalized additive models. Comput. Stat. Data Anal. 55, 2372–2387 (2011).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    66.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel inference: A Practical Information-Theoretic Approach Vol. 2 (Springer Science, 2002).MATH 

    Google Scholar 
    67.Ferrer, M., Belliure, J., Minguez, E., Casado, E. & Bildstein, K. Heat loss and site-dependent fecundity in chinstrap penguins (Pygoscelis antarctica). Polar Biol. 37, 1031–1039 (2014).Article 

    Google Scholar 
    68.Tenaza, R. Behavior and nesting success relative to nest location in Adélie Penguins (Pygoscelis adeliae). Condor 73, 81–92 (1971).Article 

    Google Scholar 
    69.Wilson, D. J. et al. South Polar Skua breeding populations in the Ross Sea assessed from demonstrated relationship with Adélie Penguin numbers. Polar Biol. 40, 577–592 (2017).Article 

    Google Scholar 
    70.Ballard, G. et al. Responding to climate change: Adélie Penguins confront astronomical and ocean boundaries. Ecology 91, 2056–2069 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.Shepherd, L. D. et al. Microevolution and mega-icebergs in the Antarctic. Proc. Natl. Acad. Sci. USA. 102, 16717–16722 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Dugger, K. M., Ainley, D. G., Lyver, P. O., Barton, K. & Ballard, G. Survival differences and the effect of environmental instability on breeding dispersal in an Adélie penguin meta-population. Proc. Natl. Acad. Sci. USA. 107, 12375–12380 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Ballance, L. T., Ainley, D. G., Ballard, G. & Barton, K. An energetic correlate between colony size and foraging effort in seabirds, an example of the Adélie penguin Pygoscelis adeliae. J. Avian Biol. 40, 279–288 (2009).Article 

    Google Scholar 
    74.Jackson, A. L., Bearhop, S. & Thompson, D. R. Shape can influence the rate of colony fragmentation in ground nesting seabirds. Oikos 111, 473–478 (2005).Article 

    Google Scholar 
    75.McDowall, P. S. & Lynch, H. J. When the ‘selfish herd’ becomes the ‘frozen herd’: Spatial dynamics and population persistence in a colonial seabird. Ecology 100, e02823 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Gilchrist, H. G. Declining thick-billed murre Uria lomvia colonies experience higher gull predation rates: An inter-colony comparison. Biol. Conserv. 87, 21–29 (1999).Article 

    Google Scholar 
    77.Danchin, E., Boulinier, T. & Massot, M. Conspecific reproductive success and breeding habitat selection: Implications for the study of coloniality. Ecology 79, 2415–2428 (1998).Article 

    Google Scholar 
    78.Valone, T. J. & Templeton, J. J. Public information for the assessment of quality: A widespread social phenomenon. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 357, 1549–1557 (2002).Article 

    Google Scholar  More

  • in

    Unraveling negative biotic interactions determining soil microbial community assembly and functioning

    1.Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth’s biogeochemical cycles. Science. 2008;320:1034–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Le Chatelier E, Nielsen T, Qin JJ, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500:541–6.PubMed 
    Article 
    CAS 

    Google Scholar 
    3.Philippot L, Raaijmakers JM, Lemanceau P, van der Putten WH. Going back to the roots: the microbial ecology of the rhizosphere. Nat Rev Microbiol. 2013;11:789–99.CAS 
    Article 

    Google Scholar 
    4.Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Mol Biol Rev. 2013;77:342–56.Article 

    Google Scholar 
    5.Jones RT, Robeson MS, Lauber CL, Hamady M, Knight R, Fierer N. A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses. ISME J. 2009;3:442–53.CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Rasche F, Knapp D, Kaiser C, Koranda M, Kitzler B, Zechmeister-Boltenstern S, et al. Seasonality and resource availability control bacterial and archaeal communities in soils of a temperate beech forest. ISME J. 2011;5:389–402.CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Goberna M, Garcia C, Verdu M. A role for biotic filtering in driving phylogenetic clustering in soil bacterial communities. Glob Ecol Biogeogr. 2014;23:1346–55.Article 

    Google Scholar 
    8.Zhou JZ, Ning DL. Stochastic community assembly: does it matter in microbial ecology? Mol Biol Rev. 2017;81:e00002–17.9.Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012;10:538–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Griffin AS, West SA, Buckling A. Cooperation and competition in pathogenic bacteria. Nature. 2004;430:1024–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15–25.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.West SA, Cooper GA. Division of labour in microorganisms: an evolutionary perspective. Nat Rev Microbiol. 2016;14:716–23.CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Foster KR, Bell T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr Biol. 2012;22:1845–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Garcia-Bayona L, Comstock LE. Bacterial antagonism in host-associated microbial communities. Science. 2018;361:eaat2456.16.Braga LPP, Spor A, Kot W, Breuil MC, Hansen LH, Setubal JC, et al. Impact of phages on soil bacterial communities and nitrogen availability under different assembly scenarios. Microbiome. 2020;8:52.17.Saleem M, Fetzer I, Harms H, Chatzinotas A. Diversity of protists and bacteria determines predation performance and stability. ISME J. 2013;7:1912–21.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Nair RR, Vasse M, Wielgoss S, Sun L, Yu YTN, Velicer GJ. Bacterial predator-prey coevolution accelerates genome evolution and selects on virulence-associated prey defences. Nat Commun. 2019;10:4301.19.Perez J, Moraleda-Munoz A, Marcos-Torres FJ, Munoz-Dorado J. Bacterial predation: 75 years and counting! Environ Microbiol. 2016;18:766–79.PubMed 
    Article 

    Google Scholar 
    20.Friedman J, Higgins LM, Gore J. Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol. 2017;1:109.21.Goldford JE, Lu NX, Bajic D, Estrela S, Tikhonov M, Sanchez-Gorostiaga A, et al. Emergent simplicity in microbial community assembly. Science. 2018;361:469–74.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Russel J, Roder HL, Madsen JS, Burmolle M, Sorensen SJ. Antagonism correlates with metabolic similarity in diverse bacteria. Proc Natl Acad Sci USA. 2017;114:10684–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Zhang JJ, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30:614–20.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.26.Engelhardt IC, Welty A, Blazewicz SJ, Bru D, Rouard N, Breuil MC, et al. Depth matters: effects of precipitation regime on soil microbial activity upon rewetting of a plant-soil system. ISME J. 2018;12:1061–71.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 2010;26:266–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.29.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Abarenkov K, Nilsson RH, Larsson KH, Alexander IJ, Eberhardt U, Erland S, et al. The UNITE database for molecular identification of fungi—recent updates and future perspectives. N Phytol. 2010;186:281–5.Article 

    Google Scholar 
    33.Faith DP. Conservation evaluation and phylogenetic diversity. Biol Conserv. 1992;61:1–10.Article 

    Google Scholar 
    34.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Ning DL, Deng Y, Tiedje JM, Zhou JZ. A general framework for quantitatively assessing ecological stochasticity. Proc Natl Acad Sci USA. 2019;116:16892–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011;5:169–72.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Muyzer G, Dewaal EC, Uitterlinden AG. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol. 1993;59:695–700.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.White TJ, Bruns TD, Lee SB, Taylor JWI. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ, editors. PCR-protocols and applications: a laboratory manual. New York, NY: Academic Press; 1990. p. 315–22.39.Bru D, Ramette A, Saby NPA, Dequiedt S, Ranjard L, Jolivet C, et al. Determinants of the distribution of nitrogen-cycling microbial communities at the landscape scale. ISME J. 2011;5:532–42.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Campbell CD, Chapman SJ, Cameron CM, Davidson MS, Potts JM. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl Environ Microbiol. 2003;69:3593–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.R Development Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.42.de Mendiburu F. Agricolae: statistical procedures for agricultural research. R Package Version. 2017;1:2–8.
    Google Scholar 
    43.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. 2018.44.Soetaert K. plot3D: plotting multi-dimensional data. R package version 1.0. 2013.45.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.46.Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12:115–21.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90.CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Letunic I, Bork P. Interactive Tree of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res. 2011;39:W475–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Chiquet J, Mariadassou M, S. R. Variational inference for sparse network reconstruction from count data. ICML. 2018;97:1162–71.
    Google Scholar 
    50.Liu H, Roeder K, Wasserman L. Stability Approach to Regularization Selection (StARS) for high dimensional graphical models. Adv Neural Inf Process Syst. 2010;31:1432–40.
    Google Scholar 
    51.Chen L, Reeve J, Zhang LJ, Huang SB, Wang XF, Chen J. GMPR: a robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ. 2018;6:e4600.52.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 
    53.Rohart F, Gautier B, Singh A, Le Cao KA. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13:e1005752.54.Singh A, Gautier B, Shannon CP, Rohart F, Vacher M, Tebutt SJ, et al. DIABLO: from multi-omics assays to biomarker discovery, an integrative approach. Bioinformatics. 2019;35:3055–62.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Calderon K, Spor A, Breuil MC, Bru D, Bizouard F, Violle C, et al. Effectiveness of ecological rescue for altered soil microbial communities and functions. ISME J. 2017;11:272–83.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Hol WHG, de Boer W, de Hollander M, Kuramae EE, Meisner A, van der Putten WH. Context dependency and saturating effects of loss of rare soil microbes on plant productivity. Front Plant Sci. 2015;6:485.57.Weber MF, Poxleitner G, Hebisch E, Frey E, Opitz M. Chemical warfare and survival strategies in bacterial range expansions. J Royal Soc Interface. 2014;11:20140172.58.Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 
    Article 

    Google Scholar 
    59.Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012;6:1007–17.CAS 
    Article 

    Google Scholar 
    60.Kurm V, van der Putten WH, de Boer W, Naus-Wiezer S, Hol WHG. Low abundant soil bacteria can be metabolically versatile and fast growing. Ecology. 2017;98:555–64.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Berns AE, Philipp H, Narres HD, Burauel P, Vereecken H, Tappe W. Effect of gamma-sterilization and autoclaving on soil organic matter structure as studied by solid state NMR, UV and fluorescence spectroscopy. Eur J Soil Sci. 2008;59:540–50.CAS 
    Article 

    Google Scholar 
    62.Ghoul M, Mitri S. The ecology and evolution of microbial competition. Trends Microbiol. 2016;24:833–45.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-Gonzalez A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.CAS 
    Article 

    Google Scholar 
    64.Jones SE, Lennon JT. Dormancy contributes to the maintenance of microbial diversity. Proc Natl Acad Sci USa. 2010;107:5881–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Kurm V, Geisen S, Hol WHG. A low proportion of rare bacterial taxa responds to abiotic changes compared with dominant taxa. Environ Microbiol. 2019;21:750–8.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Garbeva P, Hordijk C, Gerards S, de Boer W. Volatile-mediated interactions between phylogenetically different soil bacteria. Front Microbiol. 2014;5:289.67.Karimi B, Terrat S, Dequiedt S, Saby NPA, Horriguel W, Lelievre M, et al. Biogeography of soil bacteria and archaea across France. Sci Adv. 2018;4:eaat1808.68.Lewin GR, Carlos C, Chevrette MG, Horn HA, McDonald BR, Stankey RJ, et al. Evolution and ecology of actinobacteria and their bioenergy applications. Annu Rev Microbiol. 2016;70:235–54.69.Prosser JI, Nicol GW. Archaeal and bacterial ammonia-oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 2012;20:523–31.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Daims H, Lebedeva EV, Pjevac P, Han P, Herbold C, Albertsen M, et al. Complete nitrification by Nitrospira bacteria. Nature. 2015;528:504–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Sorokin DY, Luecker S, Vejmelkova D, Kostrikina NA, Kleerebezem R, Rijpstra WIC, et al. Nitrification expanded: discovery, physiology and genomics of a nitrite-oxidizing bacterium from the phylum Chloroflexi. ISME J. 2012;6:2245–56.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Bell T. Next-generation experiments linking community structure and ecosystem functioning. Environ Microbiol Rep. 2019;11:20–2.PubMed 
    Article 

    Google Scholar  More

  • in

    309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula

    Marine microbiome is considered as the largest environment on earth which has many secrets concealed into it1,2. Many marine microbes play a key role in biogeochemical cycles. However, high proportions of microbes remain uncultured in vitro3 and so instead of analysing the microbes individually, cultivation-independent genome-level characterization methods notably single-cell genomics and metagenomics are frequently being applied for microbiome analysis4. Amplicon sequencing based cultivation-independent studies are enriching the microbial diversity knowledge of various hitherto less studied environmental niche, specifically within the marine resources. However, amplicon analysis is just a preliminary step in metagenomics as it focuses only on one gene for the community diversity assessment.With the view of studying the marine microbial community for determination of its composition in terms of diversity as well as function, whole metagenomics has become the preferred approach. Recently, it has been realized that the actual understanding of metagenomics data can be obtained by individual genome binning, which eventually also enhances the microbial genome database5. This requires use of various complex computational algorithms including those relying on previous data findings viz., the supervised classifiers and the unsupervised classifiers that rely on sequence specific features like the GC content, k-mer frequency and coverage estimation for binning the genomes. Most of the recently developed tools for binning include a combined approach of both the algorithms6. Binning aids in revealing the link between the potential functional genes in a given microbiome to its taxonomy.The unique properties of the Gulfs of Kathiawar Peninsula like extreme tidal variations, different sediment texture and physicochemical variations make them an ideal place for studying the microbial diversity. Varied onshore anthropogenic activities may have imparted unique features to the microflora of the Gulfs. Study of microbial diversity and functions in the mentioned Gulfs have largely been focused on cultivation based approaches and very few molecular studies have been conducted on the shore sediments. Additionally, the presence of several on-shore industries like fertilizer, chemicals, oil refineries, power plants and ASSBRY (Alang Ship Breaking Yard) may have also influenced the deeper sediment microbiome leading to their variable gene profile7. Our previous insights into the pelagic sediment resistome profile by metagenomics approach have shown that the deeper sediments, earlier thought to be primeval are actually hosting microbes with a concerning number of resistance genes7,8. This acted as a propeller to the present study wherein we tried to look deeper into the metagenomics data of the samples collected from the Gulfs of Kathiawar Peninsula and a sample from the Arabian Sea by sorting individual prokaryoplankton genomes from the data using the binning approach.We successfully reconstructed 309 Metagenome Assembled Genomes (MAGs) from the nine sediment metagenomics sequences (Table 1) from Gulf of Khambhat (GOC), Gulf of Kutch (GOK) and Arabian Sea (A) by differential coverage approach and considering the GC percent and tetranucleotide frequencies. Out of the 309 MAGs, 39 were archaeal genomes (Online-only Table 1) and 270 were bacterial genomes (Online-only Table 2). Seventy-one were high quality drafts with a completeness of ≥90% and contamination More

  • in

    Microbial drivers of methane emissions from unrestored industrial salt ponds

    1.Costanza R, d’Arge R, de Groot R, Farber S, Grasso M, Hannon B, et al. The value of the world’s ecosystem services and natural capital. Ecol Econ. 1998;25:3–15.Article 

    Google Scholar 
    2.Grimsditch G, Alder J, Nakamura T, Kenchington R, Tamelander J. The blue carbon special edition—introduction and overview. Ocean Coast Manag. 2013;83:1–4.Article 

    Google Scholar 
    3.Duarte CM, Losada IJ, Hendriks IE, Mazarrasa I, Marbà N. The role of coastal plant communities for climate change mitigation and adaptation. Nat Clim Change. 2013;3:961–8.CAS 
    Article 

    Google Scholar 
    4.Mcleod E, Chmura GL, Bouillon S, Salm R, Björk M, Duarte CM, et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front Ecol Environ. 2011;9:552–60.Article 

    Google Scholar 
    5.Neef L, Weele M van, Velthoven P van. Optimal estimation of the present-day global methane budget. Glob Biogeochem Cycles. 2010;24:GB4024.6.Schlesinger WH, Bernhardt ES. Biogeochemistry: an analysis of global change. 3rd ed. Waltham, MA: Academic Press; 2013.7.Lessner DJ. Methanogenesis biochemistry. eLS. John Wiley & Sons, Hoboken, NJ, USA; 2009.8.Conrad R. Importance of hydrogenotrophic, aceticlastic and methylotrophic methanogenesis for methane production in terrestrial, aquatic and other anoxic environments: a mini review. Pedosphere. 2020;30:25–39.Article 

    Google Scholar 
    9.Herbert ER, Boon P, Burgin AJ, Neubauer SC, Franklin RB, Ardón M, et al. A global perspective on wetland salinization: ecological consequences of a growing threat to freshwater wetlands. Ecosphere. 2015;6:art206.Article 

    Google Scholar 
    10.Wicke B, Smeets E, Dornburg V, Vashev B, Gaiser T, Turkenburg W, et al. The global technical and economic potential of bioenergy from salt-affected soils. Energy Environ Sci. 2011;4:2669–81.Article 

    Google Scholar 
    11.Kristjansson JK, Schönheit P. Why do sulfate-reducing bacteria outcompete methanogenic bacteria for substrates? Oecologia. 1983;60:264–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Karl DM, Beversdorf L, Björkman KM, Church MJ, Martinez A, Delong EF. Aerobic production of methane in the sea. Nat Geosci. 2008;1:473–8.CAS 
    Article 

    Google Scholar 
    13.Mcgenity T, Sorokin D. Methanogens and methanogenesis in hypersaline environments. Biogenesis of hydrocarbons. Springer International Publishing, New York, NY, USA; 2018. p. 1–27.14.Repeta DJ, Ferrón S, Sosa OA, Johnson CG, Repeta LD, Acker M, et al. Marine methane paradox explained by bacterial degradation of dissolved organic matter. Nat Geosci. 2016;9:884–7.CAS 
    Article 

    Google Scholar 
    15.Oremland RS, Polcin S. Methanogenesis and sulfate reduction: competitive and noncompetitive substrates in estuarine sediments. Appl Environ Microbiol. 1982;44:1270–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.van der Gon HACD, Neue H-U. Methane emission from a wetland rice field as affected by salinity. Plant Soil. 1995;170:307–13.Article 

    Google Scholar 
    17.Gómez-Villegas P, Vigara J, León R. Characterization of the microbial population inhabiting a solar saltern pond of the Odiel Marshlands (SW Spain). Mar Drugs. 2018;16:332.PubMed Central 
    Article 
    CAS 
    PubMed 

    Google Scholar 
    18.Ley RE, Harris JK, Wilcox J, Spear JR, Miller SR, Bebout BM, et al. Unexpected diversity and complexity of the Guerrero Negro hypersaline microbial mat. Appl Environ Microbiol. 2006;72:3685–95.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Thombre RS, Shinde VD, Oke RS, Dhar SK, Shouche YS. Biology and survival of extremely halophilic archaeon Haloarcula marismortui RR12 isolated from Mumbai salterns, India in response to salinity stress. Sci Rep. 2016;6:25642.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Takekawa JY, Miles AK, Schoellhamer DH, Athearn ND, Saiki MK, Duffy WD, et al. Trophic structure and avian communities across a salinity gradient in evaporation ponds of the San Francisco Bay estuary. Hydrobiologia. 2006;567:307–27.CAS 
    Article 

    Google Scholar 
    21.Ver Planck WE. Salt in California. State of California Deparment of Natural Resources, Division of Mines. Mines Bull 175. San Francisco, CA, USA: 1958.22.Johnck EJ. The South Bay Salt Pond Restoration Project: a cultural landscape approach for the resource management plan. Sonoma State University, Rohnert Park, CA, USA; 2008.23.Ackerman JT, Marvin-DiPasquale M, Slotton D, Eagles-Smith CA, Hartman A, Agee JL, et al. The South Bay Mercury Project: using biosentinels to monitor effects of wetland restoration for the South Bay Salt Pond Restoration Project. South Bay Salt Pond Restoration Project and Resources Legacy Fund, San Francisco, CA, USA; 2013.24.Valoppi L. Phase 1 studies summary of major findings of the South Bay Salt Pond Restoration Project, South San Francisco Bay, California. Phase 1 studies summary of major findings of the South Bay Salt Pond Restoration Project, South San Francisco Bay, California. Reston, VA: U.S. Geological Survey; 2018.25.Callaway JC, Parker VT, Vasey MC, Schile LM, Herbert ER. Tidal wetland restoration in San Francisco Bay: history and current issues. San Franc Estuary Watershed Sci. 2011;9: Article 2.26.Cargill. San Francisco Bay salt ponds. Cargill, Newark, CA, USA; 2020. https://www.cargill.com/page/sf/sf-bay-salt-ponds.27.Levey JR, Vasicek P, Fricke H, Archer J, Henry RF. Salt pond SF2 restoration, wildlife, and habitat protection. American Society of Civil Engineers, Reston, VA; 2012.520−9.28.Dugan HA, Summers JC, Skaff NK, Krivak-Tetley FE, Doubek JP, Burke SM, et al. Long-term chloride concentrations in North American and European freshwater lakes. Sci Data. 2017;4:170101.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Tremblay J, Singh K, Fern A, Kirton ES, He S, Woyke T, et al. Primer and platform effects on 16S rRNA tag sequencing. Front Microbiol 2015;6:771.30.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–96.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Wang Q, Garrity GM, Tiedje JM, Cole JR. A naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 2007;73:5264−67.32.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    35.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2:26.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    39.Lin H-H, Liao Y-C. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep. 2016;6:24175.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation, and scoring strategy. Nat Microbiol. 2018;3:836–43.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Yu FB, Blainey PC, Schulz F, Woyke T, Horowitz MA, Quake SR. Microfluidic-based mini-metagenomics enables discovery of novel microbial lineages from complex environmental samples. eLife. 2017;6:e26580.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol. 2019;20:217.Article 
    CAS 

    Google Scholar 
    46.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version 2.5-6. 2019. https://CRAN.R-project.org/package=vegan.47.Vu VQ. ggbiplot: a ggplot2 based biplot. R package version 0.55. 2011. http://github.com/vqv/ggbiplot.48.De Cáceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.PubMed 
    Article 

    Google Scholar 
    49.Prestat E, David MM, Hultman J, Taş N, Lamendella R, Dvornik J, et al. FOAM (functional ontology assignments for metagenomes): a hidden Markov model (HMM) database with environmental focus. Nucleic Acids Res. 2014;42:e145.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    50.Liu J, Cade-Menun BJ, Yang J, Hu Y, Liu CW, Tremblay J, et al. Long-term land use affects phosphorus speciation and the composition of phosphorus cycling genes in agricultural soils. Front Microbiol. 2018;9:1643.51.Manor O, Borenstein E. MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome. Genome Biol. 2015;16:53.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    52.Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Girvan M, Newman MEJ. Community structure in social and biological networks. Proc Natl Acad Sci USA. 2002;99:7821–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Jurasinski G, Koebsch F, Guenther A, Beetz S. flux: flux rate calculation from dynamic closed chamber measurements. R package version 0.3-0. 2014. https://CRAN.R-project.org/package=flux.55.Culkin F, Smith N. Determination of the concentration of potassium chloride solution having the same electrical conductivity, at 15 °C and infinite frequency, as standard seawater of salinity 35.0000 ‰ (chlorinity 19.37394 ‰). IEEE J Ocean Eng. 1980;5:22–23.Article 

    Google Scholar 
    56.Kuever J. The Family Desulfohalobiaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 87–95.57.López-Pérez M, Rodriguez-Valera F. The Family Alteromonadaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Gammaproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 69–92.58.Oren A. The Order Halanaerobiales, and the Families Halanaerobiaceae and Halobacteroidaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Firmicutes and Tenericutes. Berlin, Heidelberg: Springer; 2014. p. 153−77.59.Pujalte MJ, Lucena T, Ruvira MA, Arahal DR, Macián MC. The Family Rhodobacteraceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Alphaproteobacteria and Betaproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 439–512.60.Kuever J. The Family Desulfobacteraceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 45–73.61.Kuever J. The Family Desulfobulbaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 75–86.62.Kuever J. The Family Syntrophobacteraceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Deltaproteobacteria and Epsilonproteobacteria. Berlin, Heidelberg: Springer; 2014. p. 289−99.63.Oren A. The Family Methanosarcinaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: other major lineages of bacteria and the archaea. Berlin, Heidelberg: Springer; 2014. p. 259−81.64.Bonin AS, Boone DR. The Order Methanobacteriales. In: Dworkin M, Falkow S, Rosenberg E, Schleifer K-H, Stackebrandt E (eds). The Prokaryotes: Volume 3: Archaea. Bacteria: Firmicutes, Actinomycetes. New York, NY: Springer; 2006. p. 231−43.65.Kathuria S, Martiny AC. Prevalence of a calcium-based alkaline phosphatase associated with the marine cyanobacterium Prochlorococcus and other ocean bacteria. Environ Microbiol. 2011;13:74–83.CAS 
    PubMed 
    Article 

    Google Scholar 
    66.Kamat SS, Williams HJ, Dangott LJ, Chakrabarti M, Raushel FM. The catalytic mechanism for aerobic formation of methane by bacteria. Nature. 2013;497:132–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    67.Yu X, Doroghazi JR, Janga SC, Zhang JK, Circello B, Griffin BM, et al. Diversity and abundance of phosphonate biosynthetic genes in nature. Proc Natl Acad Sci USA. 2013;110:20759–64.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front Microbiol. 2013;4:291.69.Metcalf WW, Griffin BM, Cicchillo RM, Gao J, Janga SC, Cooke HA, et al. Synthesis of methylphosphonic acid by marine microbes: a source for methane in the aerobic ocean. Science. 2012;337:1104–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Poffenbarger HJ, Needelman BA, Megonigal JP. Salinity influence on methane emissions from tidal marshes. Wetlands. 2011;31:831–42.Article 

    Google Scholar 
    71.Oremland RS, Boone DR. Methanolobus taylorii sp. nov., a new methylotrophic, estuarine methanogen. Int J Syst Bacteriol. 1994;44:573–5.Article 

    Google Scholar 
    72.Zhang G, Jiang N, Liu X, Dong X. Methanogenesis from methanol at low temperatures by a novel psychrophilic methanogen, “Methanolobus psychrophilus” sp. nov., prevalent in Zoige Wetland of the Tibetan Plateau. Appl Environ Microbiol. 2008;74:6114–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Antony CP, Murrell JC, Shouche YS. Molecular diversity of methanogens and identification of Methanolobus sp. as active methylotrophic Archaea in Lonar crater lake sediments. FEMS Microbiol Ecol. 2012;81:43–51.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.König H, Stetter KO. Isolation and characterization of Methanolobus tindarius, sp. nov., a coccoid methanogen growing only on methanol and methylamines. Zentralblatt Für Bakteriol Mikrobiol Hyg Abt Orig C Allg Angew Ökol Mikrobiol. 1982;3:478–90.
    Google Scholar 
    75.Doerfert SN, Reichlen M, Iyer P, Wang M, Ferry JG. Methanolobus zinderi sp. nov., a methylotrophic methanogen isolated from a deep subsurface coal seam. Int J Syst Evol Microbiol. 2009;59:1064–9.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Ni S, Boone DR. Isolation and characterization of a dimethyl sulfide-degrading methanogen, methanolobus siciliae HI350, from an oil well, characterization of M. siciliae T4/MT, and emendation of M. siciliae. Int J Syst Bacteriol. 1991;41:410–6.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Mochimaru H, Tamaki H, Hanada S, Imachi H, Nakamura K, Sakata S, et al. Methanolobus profundi sp. nov., a methylotrophic methanogen isolated from deep subsurface sediments in a natural gas field. Int J Syst Evol Microbiol. 2009;59:714–8.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78.Orphan VJ, Jahnke LL, Embaye T, Turk KA, Pernthaler A, Summons RE, et al. Characterization and spatial distribution of methanogens and methanogenic biosignatures in hypersaline microbial mats of Baja California. Geobiology. 2008;6:376–93.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Smith JM, Green SJ, Kelley CA, Prufert‐Bebout L, Bebout BM. Shifts in methanogen community structure and function associated with long-term manipulation of sulfate and salinity in a hypersaline microbial mat. Environ Microbiol. 2008;10:386–94.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Zhuang G-C, Elling FJ, Nigro LM, Samarkin V, Joye SB, Teske A, et al. Multiple evidence for methylotrophic methanogenesis as the dominant methanogenic pathway in hypersaline sediments from the Orca Basin, Gulf of Mexico. Geochim Cosmochim Acta. 2016;187:1–20.CAS 
    Article 

    Google Scholar 
    81.Zhuang G-C, Heuer VB, Lazar CS, Goldhammer T, Wendt J, Samarkin VA, et al. Relative importance of methylotrophic methanogenesis in sediments of the Western Mediterranean Sea. Geochim Cosmochim Acta. 2018;224:171–86.CAS 
    Article 

    Google Scholar 
    82.Oremland RS, Marsh LM, Polcin S. Methane production and simultaneous sulphate reduction in anoxic, salt marsh sediments. Nature. 1982;296:143–5.CAS 
    Article 

    Google Scholar 
    83.Wanner BL, Metcalf WW. Molecular genetic studies of a 10.9 kb operon in Escherichia coli for phosphonate uptake and biodegradation. FEMS Microbiol Lett. 1992;100:133–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    84.Dyhrman ST, Chappell PD, Haley ST, Moffett JW, Orchard ED, Waterbury JB, et al. Phosphonate utilization by the globally important marine diazotroph Trichodesmium. Nature. 2006;439:68–71.CAS 
    PubMed 
    Article 

    Google Scholar 
    85.White AK, Metcalf WW. Microbial metabolism of reduced phosphorus compounds. Annu Rev Microbiol. 2007;61:379–400.CAS 
    PubMed 
    Article 

    Google Scholar 
    86.Carini P, White AE, Campbell EO, Giovannoni SJ. Methane production by phosphate-starved SAR11 chemoheterotrophic marine bacteria. Nat Commun. 2014;5:4346.CAS 
    PubMed 
    Article 

    Google Scholar 
    87.Damm E, Helmke E, Thoms S, Schauer U, Nothig E, Bakker K, et al. Methane production in aerobic oligotrophic surface water in the central Arctic Ocean. Biogeosciences. 2010;7:1099–108.CAS 
    Article 

    Google Scholar 
    88.Martínez A, Ventouras L-A, Wilson ST, Karl DM, Delong EF. Metatranscriptomic and functional metagenomic analysis of methylphosphonate utilization by marine bacteria. Front Microbiol. 2013;4:340.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Yao M, Henny C, Maresca JA. Freshwater bacteria release methane as a by-product of phosphorus acquisition. Appl Environ Microbiol. 2016;82:6994–7003.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    90.Sosa OA, Repeta DJ, DeLong EF, Ashkezari MD, Karl DM. Phosphate-limited ocean regions select for bacterial populations enriched in the carbon–phosphorus lyase pathway for phosphonate degradation. Environ Microbiol. 2019;21:2402–14.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    91.Fisher J, Acreman MC. Wetland nutrient removal: a review of evidence. Hydrol Earth Syst Sci Discuss Eur Geosci Union. 2004;8:673–85.CAS 
    Article 

    Google Scholar 
    92.Kadlec RH. Constructed marshes for nitrate removal. Crit Rev Environ Sci Technol. 2012;42:934–1005.CAS 
    Article 

    Google Scholar 
    93.He S, Malfatti SA, McFarland JW, Anderson FE, Pati A, Huntemann M, et al. Patterns in wetland microbial community composition and functional gene repertoire associated with methane emissions. mBio. 2015;6:e00066–15.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Energetic and health effects of protein overconsumption constrain dietary adaptation in an apex predator

    1.Fuller, A. et al. Physiological mechanisms in coping with climate change. Phys. Biochem. Zool. 83, 713–720. https://doi.org/10.1086/652242 (2010).Article 

    Google Scholar 
    2.Raubenheimer, D., Simpson, S. J. & Tait, A. H. Match and mismatch: Conservation physiology, nutritional ecology and the timescales of biological adaptation. Philos. Trans. R. Soc. B 367, 1628–1646. https://doi.org/10.1098/rstb.2012.0007 (2012).CAS 
    Article 

    Google Scholar 
    3.Tracy, C. R. et al. The importance of physiological ecology in conservation biology. Integr. Comp. Biol. 46, 1191–1205. https://doi.org/10.1093/icb/icl054 (2006).Article 
    PubMed 

    Google Scholar 
    4.Parker, K. L., Barboza, P. S. & Gillingham, M. P. Nutrition integrates environmental responses of ungulates. Funct. Ecol. 23, 57–69. https://doi.org/10.1111/j.1365-2435.2009.01528.x (2009).Article 

    Google Scholar 
    5.Morris, J. G. Idiosyncratic nutrient requirements of cats appear to be diet-induced evolutionary adaptations. Nutr. Res. Rev. 15, 153–168. https://doi.org/10.1079/NRR200238 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    6.Hofmann, R. R. Evolutionary steps of ecophysiological adaptation and diversification of ruminants: A comparative view of their digestive system. Oecologia 78, 443–457. https://doi.org/10.1007/BF00378733 (1989).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Rode, K. D., Chapman, C. A., McDowell, L. R. & Stickler, C. Nutritional correlates of population density across habitats and logging intensities in redtail monkeys (Cercopithecus Ascanius). Biotropica 38, 625–634. https://doi.org/10.1111/j.1744-7429.2006.00183.x (2006).Article 

    Google Scholar 
    8.Birnie-Gauvin, K., Peiman, K. S., Raubenheimer, D. & Cooke, S. J. Nutritional physiology and ecology of wildlife in a changing world. Cons Phys. 5, cox030. https://doi.org/10.1093/conphys/cox030 (2017).CAS 
    Article 

    Google Scholar 
    9.Rode, K. D. & Robbins, C. T. Why bears consume mixed diets during fruit abundance. Can. J. Zool. 78, 1640–1645. https://doi.org/10.1139/z00-082 (2000).Article 

    Google Scholar 
    10.Robbins, C. T. et al. Optimizing protein intake as a foraging strategy to maximize mass gain in an omnivore. Oikos 116, 1675–1683. https://doi.org/10.1111/j.0030-1299.2007.16140.x (2007).Article 

    Google Scholar 
    11.Erlenbach, J. A., Rode, K. D., Raubenheimer, D. & Robbins, C. T. Macronutrient optimization and energy maximization determine diets of brown bears. J. Mamm. 95, 160–168. https://doi.org/10.1644/13-MAMM-A-161 (2014).Article 

    Google Scholar 
    12.Nie, Y. et al. Giant pandas are macronutritional carnivores. Curr. Biol. 29, 1677–1682. https://doi.org/10.1016/j.cub.2019.03.067 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    13.Sponheimer, M., Clauss, M. & Codron, D. Dietary evolution: The panda paradox. Curr. Biol. 29, R417–R419. https://doi.org/10.1016/j.cub.2019.04.045 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Stirling, I. & McEwan, E. H. The caloric value of whole ringed seals (Phoca hispida) in relation to polar bear (Ursus maritimus) ecology and hunting behavior. Can. J. Zool. 53, 1021–1027. https://doi.org/10.1139/z75-117 (1975).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Liu, S. P. et al. Population genomics reveal recent speciation and rapid evolutionary adaptation in Polar Bears. Cell 157, 785–794. https://doi.org/10.1016/j.cell.2014.03.054 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Kohl, K. D., Coogan, S. C. P. & Raubenheimer, D. Do wild carnivores forage for prey or for nutrient? Evidence for nutrient-specific foraging in vertebrate predators. BioEssays 37, 701–709. https://doi.org/10.1002/bies.201400171 (2015).Article 
    PubMed 

    Google Scholar 
    17.Machovsky-Capuska, G. E. & Raubenheimer, D. The nutritional ecology of marine apex predators. Ann. Rev. Mar. Sci. 12, 361–387. https://doi.org/10.1146/annurev-marine-010318-095411 (2020).Article 
    PubMed 

    Google Scholar 
    18.Hewson-Hughes, A. K., Colyer, A., Simpson, S. J. & Raubenheimer, D. Balancing macronutrient intake in a mammalian carnivore: Disentangling the influences of flavor and nutrition. R. Soc. Open 3, 160081. https://doi.org/10.1098/rsos.160081 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    19.McKinney, M. A., Atwood, T. C., Iverson, S. J. & Peacock, E. Temporal complexity of southern Beaufort Sea polar bear diets during a period of increasing land use. Ecosphere 8, e01633. https://doi.org/10.1002/ecs2.1633 (2017).Article 

    Google Scholar 
    20.Rode, K. D. et al. Spring fasting behavior in a marine apex predator provides an index of ecosystem productivity. Glob. Change Biol. 24, 410–423. https://doi.org/10.1111/gcb.13933 (2018).ADS 
    Article 

    Google Scholar 
    21.Rode, K. D. et al. Variation in the response of an arctic top predator experiencing habitat loss: Feeding and reproductive ecology of two polar bear populations. Glob. Change Biol. 20, 76–88. https://doi.org/10.1111/gcb.12339 (2014).ADS 
    Article 

    Google Scholar 
    22.Rode, K. D. et al. Seal body condition and atmospheric circulation patterns in the Chukchi Sea influence polar bear body condition, recruitment, and feeding ecology. Glob. Change Biol. https://doi.org/10.1111/gcb.15572 (2021).Article 

    Google Scholar 
    23.Yurkowski, D. J., Hussey, N. E., Semeniuk, C., Ferguson, S. H. & Fisk, A. T. Effects of fat extraction and the utility of fat normalization models on δ13C and δ15N values in Arctic marine mammal tissues. Pol. Biol. 38, 131–143. https://doi.org/10.1007/s00300-014-1571-1 (2014).Article 

    Google Scholar 
    24.Hilderbrand, G. V., Jenkins, S. G., Schwartz, C. C., Hanley, T. A. & Robbins, C. T. Effect of seasonal differences in dietary meat intake on changes in body mass and composition in wild and captive brown bears. Can. J. Zool. 77, 1623–1630. https://doi.org/10.1139/z99-133 (1999).Article 

    Google Scholar 
    25.McCullough, D. R. & Ullrey, D. E. Proximate mineral and gross energy composition of white-tailed deer. J. Wildl. Manag. 47, 430–441. https://doi.org/10.2307/3808516 (1983).Article 

    Google Scholar 
    26.Pritchard, G. T. & Robbins, C. T. Digestive and metabolic efficiencies of grizzly and black bears. Can. J. Zool. 68, 1645–1651. https://doi.org/10.1139/z90-244 (1990).Article 

    Google Scholar 
    27.LaDouceur, E. E. B., Garner, M. M., Davis, B. & Tseng, F. A retrospective study of end-stage renal disease in captive polar bears (Ursus maritimus). J. Zoo Wildl. Med. 45, 69–77. https://doi.org/10.1638/2013-0071R.1 (2014).Article 
    PubMed 

    Google Scholar 
    28.Derocher, A. E. & Stirling, I. Aspects of survival in juvenile polar bears. Can. J. Zool. 74, 1246–1252. https://doi.org/10.1139/z96-138 (1996).Article 

    Google Scholar 
    29.Hedberg, G. E. et al. Milk composition in free-ranging polar bears (Ursus maritimus) as a model for captive rearing milk formula. Zoo Biol. 30, 550–565. https://doi.org/10.1002/zoo.20375 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    30.Jensen, K. et al. Nutrient-specific compensatory feeding in a mammalian carnivore, the mink, Neovison vison. Br. J. Nutr. 112, 1226–1233. https://doi.org/10.1017/S0007114514001664 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Rosen, D. A. S. & Trites, A. W. Examining the potential for nutritional stress in young Stellar sea lions: Physiological effects of prey composition. J. Comp. Phys. B 175, 265–273. https://doi.org/10.1007/s00360-005-0481-5 (2005).CAS 
    Article 

    Google Scholar 
    32.Kirsch, P. E., Iverson, S. J. & Bowen, W. D. Effect of a low-fat diet on body composition and blubber fatty acids of captive juvenile harp seals (Phoca groenlandica). Phys. Biochem. Zool. 73, 45–59. https://doi.org/10.1086/316723 (2000).CAS 
    Article 

    Google Scholar 
    33.Zhao, L., Schell, D. M. & Castellini, M. A. Dietary macronutrients influence 13C and 15N signatures of pinnipeds: Captive feeding studies with harbor seals (Phoca vitulina). Physiol. Part A Mol. Integr. Phys. 143, 469–478. https://doi.org/10.1016/j.cbpa.2005.12.032 (2006).CAS 
    Article 

    Google Scholar 
    34.Diaz Gomez, M., Rosen, D. A. S. & Trites, A. W. Net energy gained by northern fur seals (Callorhinus ursinus) is impacted more by diet quality than diet diversity. Can. J. Zool. 94, 12–135. https://doi.org/10.1139/cjz-2015-0143 (2016).CAS 
    Article 

    Google Scholar 
    35.Le Bellego, L., van Milgen, J. & Noblet, J. Effect of high temperature and low-protein diets on performance of growing pigs. J. Anim. Sci. 79, 1259–1271. https://doi.org/10.2527/2001.7951259x (2002).Article 

    Google Scholar 
    36.Anton, S. D. et al. Effects of popular diets without specific calorie targets on weight loss outcomes: Systematic review of findings from clinical trials. Nutrients 9, 822. https://doi.org/10.3390/nu9080822 (2017).Article 
    PubMed Central 

    Google Scholar 
    37.Bininda-Emonds, O. R. P., Gittleman, J. L. & Purvis, A. Building large trees by combining phylogenetic information: A complete phylogeny of the extant Carnivora (Mammalia). Biol. Rev. 74, 143–175. https://doi.org/10.1017/S0006323199005307 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    38.Plantinga, E. A., Bosch, G. & Hendriks, W. H. Estimation of the dietary nutrient profile of free-roaming feral cats: Possible implications for nutrition of domestic cats. Br. J. Nutr. 106, S35–S48. https://doi.org/10.1017/S0007114511002285 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Hewson-Hughes, A. K. et al. Geometric analysis of macronutrient selection in breeds of the domestic dog, Canis lupus familiaris. Behav. Ecol. 24, 293–304. https://doi.org/10.1093/beheco/ars168 (2013).Article 
    PubMed 

    Google Scholar 
    40.Trites, A. W. & Donnelly, C. P. The decline of Steller sea lions Eumetopias jubatus in Alaska: A review of the nutritional stress hypothesis. Mamm. Rev. 33, 3–28. https://doi.org/10.1046/j.1365-2907.2003.00009.x (2003).Article 

    Google Scholar 
    41.Hauser, D. D. W., Allen, C. S., Rich, H. B. Jr. & Quinn, T. P. Resident harbor seals (Phoca vitulina) in Iliamna Lake, Alaska: Summer diet and partial consumption of adult sockeye salmon (Oncorhynchus nerka). Aquat. Mamm. 34, 303–309. https://doi.org/10.1578/AM.34.3.2008.303 (2008).Article 

    Google Scholar 
    42.Jia, Y. et al. Long-term high intake of whole proteins results in renal damage in pigs. J. Nutr. 140, 1646–1652. https://doi.org/10.3945/jn.110.123034 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Wakefield, A. P., House, J. D., Ogborn, M. R., Weiler, H. A. & Aukema, H. M. A diet of 35% of energy from protein leads to kidney damage in female Sprague–Dawley rats. Br. J. Nutr. 106, 656–663. https://doi.org/10.1017/S0007114511000730 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    44.Ko, G.-J., Rhee, C. M., Kalantar-Zadeh, K. & Joshi, S. The effects of high-protein diets on kidney health and longevity. J. Am. Soc. Nephrol. 31, 1667–1679. https://doi.org/10.1681/ASN.2020010028 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    45.Bӧswald, L. F., Kienzle, E. & Dobenecker, B. Observation about phosphorus and protein supply in cats and dogs prior to the diagnosis of chronic kidney disease. J. Phys. Anim. Nutr. 102, 31–36. https://doi.org/10.1111/jpn.12886 (2017).CAS 
    Article 

    Google Scholar 
    46.Ioannou, G. N., Morrow, O. B., Connole, M. L. & Lee, S. P. Association between dietary nutrient composition and the incidence of cirrhosis or liver cancer in the united states population. Hepatology 50, 175–184. https://doi.org/10.1002/hep.22941 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    47.Tryland, M. et al. Plasma biochemical values from apparently healthy free-ranging polar bears from Svalbard. J. Wildl. Dis. 38, 566–575. https://doi.org/10.7589/0090-3558-38.3.566 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    48.Thiemann, G. W., Iverson, S. J. & Stirling, I. Polar bear diets and Arctic marine food webs: Insights from fatty acid analysis. Ecol. Monogr. 78, 591–613. https://doi.org/10.1890/07-1050.1 (2008).Article 

    Google Scholar 
    49.Ryg, M., Smith, T. G. & Oritsland, N. A. Seasonal changes in body mass and body composition of ringed seals (Phoca hispida) on Svalbard. Can. J. Zool. 68, 470–475. https://doi.org/10.1139/z90-069 (1990).Article 

    Google Scholar 
    50.Ferguson, S. H. et al. Demographic, ecological, and physiological responses of ringed seals to an abrupt decline in sea ice availability. Peer J. 5, e2957. https://doi.org/10.7717/peerj.2957 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Atwood, T. C. et al. Rapid environmental change drives increased land use by an Arctic marine predator. PLoS One 11, 30155932. https://doi.org/10.1371/journal.pone.0155932 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    52.Molnar, P. K. et al. Fasting season length sets temporal limits for global polar bear persistence. Nat. Clim. Change 10, 732–738. https://doi.org/10.1038/s41558-020-0818-9 (2020).ADS 
    Article 

    Google Scholar 
    53.Rode, K. D., Robbins, C. T., Nelson, L. & Amstrup, S. C. Can polar bears use terrestrial foods to offset lost ice-based hunting opportunities?. Front. Ecol. Environ. 13, 138–145. https://doi.org/10.1890/140202 (2015).Article 

    Google Scholar 
    54.McArt, S. H. et al. Summer nitrogen availability as a bottom-up constraint on moose in south-central Alaska. Ecology 90, 1400–1411. https://doi.org/10.1890/08-1435.1 (2009).Article 
    PubMed 

    Google Scholar 
    55.Lahtinen, M., Clinnick, D., Mannermaa, K., Salonen, J. S. & Viranta, S. Excess protein enabled dog domestication during severe Ice Age winters. Sci. Rep. 11, 7. https://doi.org/10.1038/s41598-020-78214-4 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Regehr, E. V., Hostetter, N. J., Wilson, R. R. & Rode, K. D. Integrated population modeling provides the first empirical estimates of vital rates and abundance for polar bears in the Chukchi Sea. Sci. Rep. 8, 16780. https://doi.org/10.1038/s41598-018-34824-7 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Crawford, J. A., Quakenbush, L. T. & Citta, J. J. A comparison of ringed seal and bearded seal diet, condition, and productivity between historical (1975–19480 and recent (2003–2012) periods in the Alaskan Bering and Chukchi Seas. Progr. Oceanogr. 136, 133–150. https://doi.org/10.1016/j.pocean.2015.05.011 (2015).ADS 
    Article 

    Google Scholar 
    58.Germain, L. R., McCarthy, M. D., Koch, P. L. & Harvey, J. T. Stable carbon and nitrogen isotopes in multiple tissues of wild and captive harbor seals (Phoca vitulina) off the California coast. Mar. Mamm. Sci. 28, 542–560. https://doi.org/10.1111/j.1748-7692.2011.00516.x (2011).CAS 
    Article 

    Google Scholar 
    59.Erlenbach, J. A. Nutritional and landscape ecology of brown bears (Ursus arctos). PhD dissertation. Washington State University, Pullman, WA, USA (2020).60.Laidre, K. L., Stirling, I., Estes, J. A., Kochnev, A. & Roberts, J. Historical and potential future importance of large whales as food for polar bears. Front. Ecol. Environ. 16, 515–524. https://doi.org/10.1002/fee.1963 (2018).Article 

    Google Scholar 
    61.Newsome, S. D., Koch, P. L., Etnier, M. A. & Aurioles-Gamboa, D. Using carbon and nitrogen isotope values to investigate maternal strategies in northeast Pacific otariids. Mar. Mamm. Sci. 22, 556–572. https://doi.org/10.1111/j.1748-7692.2006.00043.x (2006).Article 

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

    Google Scholar 
    63.Rode, K. D. et al. Isotopic incorporation and the effects of fasting and dietary fat content on isotopic discrimination in large carnivorous mammals. Phys. Biochem. Zool. 89, 182–197. https://doi.org/10.1086/686490 (2016).CAS 
    Article 

    Google Scholar 
    64.Merrill, A. L. & Watt, B. K. Energy Value of Foods: Basis and Derivation, Revised. Agriculture Handbook 74 (United States Department of Agriculture, 1973).65.Dyck, M. G. & Morin, P. In vivo digestibility trials of a captive polar bear (Ursus maritimus) feeding on harp seal (Pagophilus growenlandicus) and Arctic charr (Salvelinus alpinus). Pak. J. Zool. 43, 759–767 (2011).CAS 

    Google Scholar  More

  • in

    Tracking the rising extinction risk of sharks and rays in the Northeast Atlantic Ocean and Mediterranean Sea

    1.McClenachan, L., Cooper, A. B., Carpenter, K. E. & Dulvy, N. K. Extinction risk and bottlenecks in the conservation of charismatic marine species. Conserv. Lett. 5, 1–8 (2011).
    Google Scholar 
    2.Dulvy, N. K., Jennings, S., Rogers, S. I. & Maxwell, D. L. Threat and decline in fishes: An indicator of marine biodiversity. Can. J. Fish. Aquat. Sci. 63, 1267–1275 (2006).Article 

    Google Scholar 
    3.CBD & UNEP. Strategic Plan for Biodiversity 2011–2020 and the Aichi Targets ‘Living in Harmony with Nature’. 2pp. https://www.cbd.int/doc/strategic-plan/2011-2020/Aichi-Targets-EN.pdf (Secretariat of the Convention on Biological Diversity, Montreal, Quebec, 2011).4.Butchart, S. H. M. et al. Improvements to the Red List Index. PLoS ONE 2, 1–8 (2007).Article 

    Google Scholar 
    5.Butchart, S. H. M. et al. Measuring global trends in the status of biodiversity: Red List Indices for birds. PLoS Biol. 2, 2294–2304 (2004).CAS 
    Article 

    Google Scholar 
    6.Butchart, S. H. M. et al. Using Red List Indices to measure progress towards the 2010 target and beyond. Philos. Trans. R. Soc. B Biol. Sci. 360, 255–268 (2005).CAS 
    Article 

    Google Scholar 
    7.Hoffmann, M. et al. The changing fates of the world’s mammals. Philos. Trans. R. Soc. B Biol. Sci. 366, 2598–2610 (2011).Article 

    Google Scholar 
    8.Marler, P. N. & Marler, T. E. An assessment of Red List data for the cycadales. Trop. Conserv. Sci. 8, 1114–1125 (2015).Article 

    Google Scholar 
    9.Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science (80-. ) 321, 560–563 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    10.Gärdenfors, U. Classifying threatened species at national versus global levels. Trends Ecol. Evol. 16, 511–516 (2001).Article 

    Google Scholar 
    11.Szabo, J. K., Butchart, S. H. M., Possingham, H. P. & Garnett, S. T. Adapting global biodiversity indicators to the national scale: A Red List Index for Australian birds. Biol. Conserv. 148, 61–68 (2012).Article 

    Google Scholar 
    12.Juslén, A., Hyvärinen, E. & Virtanen, L. K. Application of the Red-List Index at a national level for multiple species groups. Conserv. Biol. 27, 398–406 (2013).PubMed 
    Article 

    Google Scholar 
    13.Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science (80-. ) 330, 1503–1509 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    14.IPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. 56pp. https://ipbes.net/document-library-catalogue/summary-policymakers-global-assessment-laid-out (IPBES Secretariat, Bonn, Germany, 2019).15.Dulvy, N. K., Sadovy, Y. & Reynolds, J. D. Extinction vulnerability in marine populations. Fish Fish. 4, 25–64 (2003).Article 

    Google Scholar 
    16.Lawson, J. M. et al. Global extinction risk and conservation of Critically Endangered angel sharks in the Eastern Atlantic and Mediterranean Sea. Int. Counc. Explor. Seas J. Mar. Sci. 77, 12–29 (2020).
    Google Scholar 
    17.WGEF. Report of the Working Group on Elasmobranch Fishes (WGEF). 671pp. https://www.ices.dk/community/groups/pages/wgef.aspx (ICES CM, Lisbon, Portugal, 2018).18.Dulvy, N. K., Allen, D. J., Ralph, G. M. & Walls, R. H. L. The conservation status of sharks, rays and chimaeras in the Mediterranean Sea. 14pp. https://portals.iucn.org/library/node/47636 (IUCN, Malaga, Spain, 2016)19.Cavanagh, R. D. & Gibson, C. Overview of the Conservation Status of Cartilaginous Fishes (Chondrichthyans) in the Mediterranean Sea (IUCN, 2007). https://doi.org/10.2305/IUCN.CH.2007.MRA.3.en.Book 

    Google Scholar 
    20.Gibson, C., Valenti, S. V., Fowler, S. L. & Fordham, S. V. The Conservation Status of Northeast Atlantic Chondrichthyans: Report of the IUCN Shark Specialist Group Northeast Atlantic Regional Red List Workshop (IUCN Species Survival Commission Shark Specialist Group, 2008).
    Google Scholar 
    21.Nieto, A. et al. European Red List of Marine Fishes. 88pp. https://doi.org/10.2779/082723 (Publications Office of the European Union, Luxembourg, 2015).22.Barrett, J. H., Locker, A. M. & Roberts, C. M. The origins of intensive marine fishing in medieval Europe: The English evidence. Proc. R. Soc. B Biol. Sci. 271, 2417–2421 (2004).Article 

    Google Scholar 
    23.Rousseau, Y., Watson, R. A., Blanchard, J. L. & Fulton, E. A. Evolution of global marine fishing fleets and the response of fished resources. Proc. Natl. Acad. Sci. U.S.A. 116, 12238–12243 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Fernandes, P. G. et al. Coherent assessments of Europe’s marine fishes show regional divergence and megafauna loss. Nat. Ecol. Evol. 1, 1–9 (2017).Article 

    Google Scholar 
    25.Walls, R. H. L. & Dulvy, N. K. Eliminating the dark matter of data deficiency by predicting the conservation status of Northeast Atlantic and Mediterranean Sea sharks and rays. Biol. Conserv. 246, 1–14 (2020).Article 

    Google Scholar 
    26.Kyne, P. M. et al. The thin edge of the wedge: Extremely high extinction risk in wedgefishes and giant guitarfishes. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 1337–1361 (2020).Article 

    Google Scholar 
    27.Jennings, S., Reynolds, J. D. & Mills, S. C. Life history correlates of responses to fisheries exploitation. Proc. R. Soc. B Biol. Sci. 265, 333–339 (1998).Article 

    Google Scholar 
    28.Frisk, M. G., Miller, T. J. & Fogarty, M. J. Estimation and analysis of biological parameters in elasmobranch fishes: A comparative life history study. Can. J. Fish. Aquat. Sci. 58, 969–981 (2001).Article 

    Google Scholar 
    29.Hutchings, J. A., Myers, R. A., García, V. B., Lucifora, L. O. & Kuparinen, A. Life-history correlates of extinction risk and recovery potential. Ecol. Appl. 22, 1061–1067 (2012).PubMed 
    Article 

    Google Scholar 
    30.Dulvy, N. K. et al. Extinction risk and conservation of the world’s sharks and rays. Elife 3, 1–35 (2014).Article 

    Google Scholar 
    31.Cardoso, P. Package ‘red’. 32pp. Available at: https://cran.r-project.org/web/packages/red/red.pdf (2020).32.Pacoureau, N. et al. Half a century of global decline in oceanic sharks and rays. Nature 589, 567–571 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    33.IUCN 2021. The IUCN Red List of Threatened Species. Version 2021–1. Web page: https://www.iucnredlist.org. Accessed 7 April 2021.34.European Environment Agency. Red List Index for European species. 23pp. https://www.eea.europa.eu/data-and-maps/indicators/red-list-index-for-european-species/red-list-index-for-european (EEA, Copenhagen, Denmark, 2010).35.Bolam, F. C. et al. How many bird and mammal extinctions has recent conservation action prevented?. bioRxiv https://doi.org/10.1101/2020.02.11.943902 (2020).Article 

    Google Scholar 
    36.GFCM. Recommendation GFCM/29/2005/1 on the Management of Certain Fisheries Exploiting Demersal and Deep-Water Species and the Establishment of a Fisheries Restricted Area Below 1000 m. 2pp. https://www.cbd.int/doc/meetings/mar/soiom-2016-01/other/soiom-2016-01-gfcm-02-en.pdf (2005).37.Morato, T., Watson, R., Pitcher, T. J. & Pauly, D. Fishing down the deep. Fish Fish. 7, 23–33 (2006).Article 

    Google Scholar 
    38.Abernethy, K. E., Trebilcock, P., Kebede, B., Allison, E. H. & Dulvy, N. K. Fuelling the decline in UK fishing communities?. ICES J. Mar. Sci. 67, 1076–1085 (2010).Article 

    Google Scholar 
    39.Campana, S. E. Transboundary movements, unmonitored fishing mortality, and ineffective international fisheries management pose risks for pelagic sharks in the Northwest Atlantic. Can. J. Fish. Aquat. Sci. 73, 1599–1607 (2016).Article 

    Google Scholar 
    40.Queiroz, N. et al. Global spatial risk assessment of sharks under the footprint of fisheries. Nature 572, 461–466 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    41.Fredston-Hermann, A., Selden, R., Pinsky, M., Gaines, S. D. & Halpern, B. S. Cold range edges of marine fishes track climate change better than warm edges. Glob. Change Biol. 26, 2908–2922 (2020).ADS 
    Article 

    Google Scholar 
    42.Yan, H. F. et al. Overfishing and habitat loss drives range contraction of iconic marine fishes to near extinction. Sci. Adv. 7, 1–11 (2021).Article 

    Google Scholar 
    43.De Oliveira, J. A., Ellis, J. R. & Dobby, H. Incorporating density dependence in pup production in a stock assessment of NE Atlantic spurdog Squalus acanthias. ICES J. Mar. Sci. 70, 1341–1353 (2013).Article 

    Google Scholar 
    44.Bailey, D. M., Collins, M. A., Gordon, J. D. M., Zuur, A. F. & Priede, I. G. Long-term changes in deep-water fish populations in the northeast Atlantic: A deeper reaching effect of fisheries?. Proc. R. Soc. B Biol. Sci. 276, 1965–1969 (2009).CAS 
    Article 

    Google Scholar 
    45.IUCN. IUCN Red List Categories and Criteria: Version 3.1. Second Edition. iv + 32pp. https://www.iucnredlist.org/resources/grid (IUCN, Gland, Switzerland and Cambridge, UK, 2012).46.Godfray, H. C. J. et al. Food security: The challenge of feeding 9 billion people. Science (80-. ) 327, 812–818 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    47.Smith, A. D. M. & Garcia, S. M. Fishery management: Contrasts in the Mediterranean and the Atlantic. Curr. Biol. 24, 810–812 (2014).Article 
    CAS 

    Google Scholar 
    48.Caddy, J. F. Practical issues in choosing a framework for resource assessment and management of Mediterranean and Black Sea fisheries. Mediterr. Mar. Sci. 10, 83–119 (2009).Article 

    Google Scholar 
    49.Colloca, F. et al. Rebuilding Mediterranean fisheries: A new paradigm for ecological sustainability. Fish Fish. 14, 89–109 (2013).Article 

    Google Scholar 
    50.Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    51.Brander, K. Disappearance of common skate Raia batis from Irish Sea. Nature 290, 48–49 (1981).ADS 
    Article 

    Google Scholar 
    52.Vasilakopoulos, P., Maravelias, C. D. & Tserpes, G. The alarming decline of Mediterranean fish stocks. Curr. Biol. 24, 1643–1648 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Oliver, S., Braccini, M., Newman, S. J. & Harvey, E. S. Global patterns in the bycatch of sharks and rays. Mar. Policy 54, 86–97 (2015).Article 

    Google Scholar 
    54.Harrison, A. L. et al. The political biogeography of migratory marine predators. Nat. Ecol. Evol. 2, 1571–1578 (2018).PubMed 
    Article 

    Google Scholar 
    55.White, C. & Costello, C. Close the high seas to fishing?. PLoS Biol. 12, 1–5 (2014).Article 
    CAS 

    Google Scholar 
    56.Donald, P. F. et al. International conservation policy delivers benefits for birds in Europe. Science (80-. ) 317, 810–813 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    57.Demirel, N., Zengin, M. & Ulman, A. First large-scale eastern Mediterranean and black sea stock assessment reveals a dramatic decline. Front. Mar. Sci. 7, 1–13 (2020).Article 

    Google Scholar 
    58.Clarke, M. W. Sharks, skates and rays in the northeast Atlantic: Population status, advice and management. J. Appl. Ichthyol. 25, 3–8 (2009).Article 

    Google Scholar 
    59.Abudaya, M. et al. Speak of the devil ray (Mobula mobular) fishery in Gaza. Rev. Fish Biol. Fish. 28, 229–239 (2018).Article 

    Google Scholar 
    60.CMS. Appendices I and II of the Convention on the Conservation of Migratory Species of Wild Animals (CMS). 16pp. Available at: https://www.cms.int/en/species/appendix-i-ii-cms (2020).61.Jabado, R. W. et al. The Conservation Status of Sharks, Rays, and Chimaeras in the Arabian Sea and Adjacent Waters (Environment Agency – Abu Dhabi, UAE and IUCN Species Survival Commission Shark Specialist Group, 2017).
    Google Scholar 
    62.Iglésias, S. P., Toulhoat, L. & Sellos, D. Y. Taxonomic confusion and market mislabelling of threatened skates: Important consequences for their conservation status. Aquat. Conserv. Mar. Freshw. Ecosyst. 20, 319–333 (2010).Article 

    Google Scholar 
    63.Ferretti, F., Myers, R. A., Serena, F. & Lotze, H. K. Loss of large predatory sharks from the Mediterranean Sea. Conserv. Biol. 22, 952–964 (2008).PubMed 
    Article 

    Google Scholar 
    64.Iglésias, S. P. & Mollen, F. H. Cold case: The early disappearance of the Bramble shark (Echinorhinus brucus) in European and adjacent waters. Oceans Past News 10, 1–5 (2018).
    Google Scholar 
    65.IUCN. Guidelines for Application of IUCN Red List Criteria at Regional and National Levels: Version 4.0 (IUCN, 2012).
    Google Scholar 
    66.CBD. Indicators for Assessing Progress Towards the 2010 Target: Change in Status of Threatened Species. Convention on Biological Diversity, UNEP/CBD/AHTEG-2010-Ind/1/INF/9. 10pp. Available at: https://www.cbd.int/meetings/TEGIND-01 (2004).67.Brooks, T. M. et al. Harnessing biodiversity and conservation knowledge products to track the Aichi targets and sustainable development goals. Biodiversity 16, 157–174 (2015).Article 

    Google Scholar 
    68.Bland, L. M. et al. Toward reassessing data-deficient species. Conserv. Biol. 31, 531–539 (2017).PubMed 
    Article 

    Google Scholar 
    69.Bland, L. M. et al. Cost-effective assessment of extinction risk with limited information. J. Appl. Ecol. 52, 861–870 (2015).Article 

    Google Scholar 
    70.White, W. T., Kyne, P. M. & Harris, M. Lost before found: A new species of whaler shark Carcharhinus obsolerus from the Western Central Pacific known only from historic records. PLoS ONE 14, 1–24 (2019).
    Google Scholar 
    71.Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

    Google Scholar 
    72.IUCN. IUCN Red List Categories and Criteria: Version 3.1. IUCN Species Survival Commission (IUCN, 2001).
    Google Scholar 
    73.Regan, T. J. et al. The consistency of extinction risk classification protocols. Conserv. Biol. 19, 1969–1977 (2005).Article 

    Google Scholar 
    74.Hiddink, J. G., Shepperson, J., Bater, R., Goonesekera, D. & Dulvy, N. K. Near disappearance of the Angelshark Squatina squatina over half a century of observations. Conserv. Sci. Pract. 1, 1–9 (2019).Article 

    Google Scholar 
    75.Bom, R. A., van de Water, M., Camphuysen, K. C. J., van der Veer, H. W. & van Leeuwen, A. The historical ecology and demise of the iconic Angelshark Squatina squatina in the southern North Sea. Mar. Biol. 167, 1–10 (2020).Article 

    Google Scholar 
    76.Shephard, S., Wögerbauer, C., Green, P., Ellis, J. R. & Roche, W. K. Angling records track the near extirpation of angel shark Squatina squatina from two Irish hotspots. Endanger. Species Res. 38, 153–158 (2019).Article 

    Google Scholar 
    77.Martin, C. S. et al. Spatio-temporal patterns in demersal elasmobranchs from trawl surveys in the eastern English Channel (1988–2008). Mar. Ecol. Prog. Ser. 417, 211–228 (2010).ADS 
    Article 

    Google Scholar 
    78.Burt, G. J., Ellis, J. R., Harley, B. F. & Kupschus, S. The FV Carhelmar Beam Trawl Survey of the Western English Channel (1989–2011): History of the Survey, Data Availability and the Distribution and Relative Abundance of Fish and Commercial Shellfish. CEFAS, Norwich, UK. Science Series, Technical Report no. 151. 139pp. (2013).79.Marandel, F., Lorance, P. & Trenkel, V. M. Determining long-term changes in a skate assemblage with aggregated landings and limited species data. Fish. Manag. Ecol. 26, 365–373 (2019).Article 

    Google Scholar 
    80.IUCN Standards and Petitions Subcommittee. Guidelines for Using the IUCN Red List Categories and Criteria, Version 11. Prepared by the Standards and Petitions Subcommittee. 87pp. Available at: http://www.iucnredlist.org/documents/RedListGuidelines.pdf (2014).81.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.r-project.org/. (2018).82.Bates, D. et al. Package ‘lme4’. 126pp. Available at: https://cran.r-project.org/web/packages/lme4/index.html (2020).83.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R Statistics for Biology and Health (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6.Book 
    MATH 

    Google Scholar 
    84.Zuur, A. F., Hilbe, J. M. & Ieno, E. N. A Beginner’s Guide to GLM and GLMM with R: A Frequentist and Bayesian Perspective for Ecologists (Highland Statistic Ltd., 2013).
    Google Scholar 
    85.Gelman, A. Scaling regression inputs by dividing by two standard deviations. Stat. Med. 27, 2865–2873 (2008).MathSciNet 
    PubMed 
    Article 

    Google Scholar 
    86.Hurvich, C. M. & Tsai, C.-L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    87.Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 
    Article 

    Google Scholar 
    88.Bartoń, K. Package ‘MuMIn’. 75pp. Available at: https://cran.r-project.org/web/packages/MuMIn/index.html (2019).89.John, A. et al. Package ‘car’. 149pp. Available at: https://cran.r-project.org/web/packages/car/index.html (2020).90.Zuur, A. F., Ieno, E. N. & Smith, G. M. Analysing Ecological Data. Statistics for Biology and Health (Springer, 2007). https://doi.org/10.1198/016214508000000715.Book 
    MATH 

    Google Scholar  More

  • in

    Climatic suitability of the eastern paralysis tick, Ixodes holocyclus, and its likely geographic distribution in the year 2050

    Tick paralysis is a common tick-borne illness in humans and animals throughout the world, caused by neurotoxins produced in the salivary glands of ticks and secreted into a host during the course of feeding by females and immature stages19. Fifty-nine ixodid and fourteen argasid ticks are currently believed to be involved in the transmission of tick paralysis worldwide19, 20. In Australia, I. holocyclus is considered to be the leading tick species implicated in the transmission of tick paralysis primarily in dogs, but also other species, viz. cats, sheep, cattle, goats, swine and horses. Humans are also occasionally affected, and the disease can be fatal2, 21. A second tick species, I. cornuatus has also been implicated in the transmission of tick paralysis in Australia; however, it is also considered a minor player in this disease22. Given the differences in their biology, distribution, and natural history of these two species, we focused on estimating the spatial distribution of I. holocyclus in the present study. We recognize, however, that it is important to consider the distributions of both species for proper epidemiological planning and management of tick paralysis in Australia.Ecological niche modeling is a well-tested approach for estimating species distributions based on abiotic factors13, 23. Several new recommendations have been made in recent years for proper construction of niche models; such as the appropriate thinning of occurrence data24, consideration of an accessible area for a species being studied (M)25, thorough exploration of model complexity26, 27, and use of multiple statistical criteria for model selection28, 29. We carefully considered all these recommendations to produce a robust spatial distribution model for I. holocyclus. The resulting replicated models were fairly consistent in predicting suitability for I. holocyclus, as indicated by moderate range estimates (Fig. 2B). Further, the MOP analysis indicated satisfactory performance of the present-day model with extrapolation only in small areas outside the predicted suitable areas. These qualities, along with the model’s very low omission rate (0.044%) gives high confidence in the predicted suitable area for this species in Australia. It will be essential, however, to confirm the actual presence of I. holocyclus outside the traditionally known areas through acarological surveys to assess our findings.The present-day spatial distribution predicted in this study (Fig. 2A) indicates that the geographic areas suitable for I. holocyclus match the currently known distribution of this species along the eastern seaboard, but the suitability also extends through most of the coastal areas in the south, and up to the Kimbolton Peninsula in Western Australia in the north. Highly suitable areas are present around and south of Perth, extending towards Albany, Western Australia. Most areas in Tasmania are also highly suitable for this species. The current distribution in the Eastern Seaboard may be wider than the traditionally known extents in some areas compared to Roberts30. It is likely that I. holocyclus will succeed in establishing permanent populations if introduced into areas that are currently free of them along the southern and northern coasts, and along the southwestern coast of Western Australia and Tasmania. Appropriate prevention of tick movement including pet inspections and quarantine will be necessary to avoid introductions.Future potential distribution of I. holocyclus in year 2050 based on both low- and high-emissions scenarios indicate moderate increases in climatic suitability from the present-day prediction (Fig. 4A,B); but noticeably also moderate to low loss of climatically suitable areas in 2050. This loss could be at least partly attributed to potential future temperature and precipitation conditions exceeding suitable ranges for these ticks in these areas, limiting their ability to survive. Predicted loss of suitable areas in future can also be observed to be irregular, and in some areas, particularly along northern Queensland and in Northern Territory, enveloped between stretches of suitable areas. Our use of relatively coarse resolution data (1 km2) limits our ability to thoroughly interpret such phenomenon, but this is likely due to variations in the geography in these areas that respond differently to future climate, as well as the potential increase in ocean temperature and subsequent influences on areas along the coast that may render them unsuitable for this species. Despite the noticeable loss in climatically suitable areas, likely no net loss in area will accrue for this species by 2050.Teo et al.31 assessed present and future potential distribution for I. holocyclus using both CLIMEX32, 33 and a novel, as-yet unpublished “climatic-range” approach to determine the suitability on monthly intervals. CLIMEX allows users to specify different upper and lower thresholds for climatic parameters, some of which were derived for their study from laboratory evaluations of I. holocyclus34. The present-day distribution reported in that study resembles our results in identification of a relatively narrow area along the East Coast as suitable; however, much of the northern and northeastern areas along the coast, the coasts of South Australia and southwestern Australia, and Tasmania are reported unsuitable. Their future predictions (2050) of the species’ potential distribution were based on two GCMs (CSIRO MK3 and MIROC-H) climate models, were also markedly different from our predictions, anticipating rather dramatic distributional loss for the species. Such model transfers are challenging, with many factors potentially producing inconsistencies35. However, the two studies reflect two fundamentally different classes of ecological niche models; CLIMEX is deterministic, whose predictions are largely constrained by user supplied threshold values for model inputs of physiological tolerance limits of a species33, whereas Maxent is a machine-learning correlative approach, in which known occurrences of a species is used in conjunction with environmental layers to determine conditions that meet a species’ environmental requirements, and therefore the suitability of geographic spaces. Although the former (CLIMEX) approach is appealing conceptually, scaling environmental dimensions between the micro-scales of physiological measurements and the macro-scales of geography is well-known to present practical and conceptual challenges36.Different ixodid ticks employ different life-history strategies in response to adverse environmental conditions, including behavioral adaptations, active uptake of atmospheric moisture, restriction of water-loss, and tolerance towards extreme temperatures37. Precisely which of these mechanisms I. holocyclus utilizes, if any at all, for its survival during diverse temperature and humidity conditions is not clearly known, but it is likely to involve multiple mechanisms. In this sense, the threshold values used by Teo et al.31, based purely on laboratory observations may have been overly restrictive, leading to a conservative distributional estimate for this species. Further, because relationships between abiotic variables and species’ occurrences are fairly complex and highly dimensional, a physiological thresholding approach wherein values are set independently for different abiotic parameters may not capture species’ relationships with environments adequately. The correlative approaches employed in the present study are data-driven, and as such may capture more of this complexity, with fewer problems of scaling across orders of magnitude of space and time.In conclusion, ticks are poikilothermic ectoparasites, whose survival, reproduction and other biological functions are regulated by ambient climatic conditions. Although ixodid ticks are known to regulate their body temperatures by moving about their habitat (vegetation), attempts to model their spatial distribution has resulted in models largely based on climate variables. Nevertheless, other factors such as host availability play a significant role in tick distribution, which unfortunately cannot be readily included in correlative ecological niche models largely because such data are rarely available. These suitability predictions, in addition to being entirely based on large-scale climate, also do not reveal the highly likely heterogeneity in abundance or density in different geographic areas within the realized climatically suitable areas. For these reasons, the distribution maps produced in this study must be used with some caution, and perhaps as a guide to target sampling and not as a substitute for thorough acarological surveys. More