More stories

  • in

    Inferring the ecological niche of bat viruses closely related to SARS-CoV-2 using phylogeographic analyses of Rhinolophus species

    Genetic analyses of Rhinolophus species identified as reservoirs of viruses closely related to SARS-CoV-2Until now, SCoV2rCs have been found in four bat species of the genus Rhinolophus: R. acuminatus, R. affinis, R. malayanus, and R. shameli. The haplotype networks constructed using CO1 sequences of these four species are shown in Fig. 3. A star-like genetic pattern, characterized by one dominant haplotype and several satellite haplotypes was found for the two bat species endemic to Southeast Asia, i.e. R. acuminatus and R. shameli.Figure 3Haplotype networks based on CO1 sequences of the four Rhinolophus species found positive for viruses closely related to SARS-CoV-2 (SCoV2rCs). The networks were constructed with the median joining method available in PopART 1.513 and modified under Adobe Illustrator CS6 (version 16.0). The codes used for the countries are the following: B (Myanmar), C (Cambodia), Ch (China), I (Indonesia), L (Laos), M (Malaysia), T (Thailand), and V (Vietnam). Colours indicate the geographic origin of haplotypes according to Fig. 2 (see online supplementary Table S1). The circles indicate haplotypes separated by at least one mutation. The black lines on the branches show the number of mutations ≥ 2. Black circles represent missing haplotypes. Circle size is proportional to the number of haplotypes. Haplogroups separated by more than seven mutations (pairwise nucleotide distances  > 1%) are highlighted by dotted lines. The red arrows show the positions of the nine bats found positive for SCoV2rCs.Full size imageIn the network of R. acuminatus, the most common haplotype (named Rac1 in online supplementary Table S1) was found in northern Cambodia, southern Laos, eastern Thailand and southern Vietnam, indicating recent gene flow among these populations. Since a virus related to SARS-CoV-2 (91.8% of genome identity), named RacCS203, was detected in five R. acuminatus bats caught in eastern Thailand in June 20206, the genetic pattern obtained for this species suggests that viruses closely related to RacCS203 may have circulated in most southern regions of mainland Southeast Asia. In contrast, R. acuminatus bats collected in Borneo (M5) showed a divergent haplotype (separated by 12 mutations; haplogroup II), suggesting that the South China Sea between mainland Southeast Asia and Borneo constitutes a barrier to gene flow. Isolated populations of R. acuminatus described in northern Myanmar, Indonesia (Java and Sumatra) and the Philippines14 should be further studied.The network of R. shameli shows a typical star-like pattern, the most common haplotype (named Rsh1 in online supplementary Table S1) being detected in northern Cambodia and Laos. Since a virus related to SARS-CoV-2 (93.1% of genome identity), named RshSTT200, was recently discovered in two R. shameli bats collected in northern Cambodia in December 20107, the genetic pattern obtained for this species suggests that viruses closely related to RshSTT200 may have circulated, at least in the zone between northern Cambodia and central Laos. The bats sampled south to the Tonle Sap lake (n = 4; southern Cambodia and Vietnamese island of Phu Quoc) were found to be genetically isolated from northern populations (four mutations). However, further sampling in the south is required to confirm this result, as it may reveal CO1 sequences identical to the haplotypes detected in the north.For the two species distributed in both China and Southeast Asia, i.e. R. affinis and R. malayanus, the genetic patterns are more complex with different haplogroups showing more than 1% of nucleotide divergence. In the network of R. affinis, there are three major haplogroups (named I, II and III in Fig. 3) separated by a minimum of seven mutations. The results are therefore in agreement with those previously published using CO1 and D-loop mitochondrial sequences15. The CO1 haplotypes detected in the localities sampled in southern China (ch1, ch4, ch5) are distantly related to the single haplotype available for central China (ch6), but they are also found in Laos, northern and central Vietnam, northern Thailand and northeastern Myanmar. This result suggests recent gene flow between populations from southern Yunnan and those from northern mainland Southeast Asia. Since a virus related to SARS-CoV-2 (96.2% of genome identity), named RaTG13, was detected in one R. affinis bat captured in southern Yunnan in 20131, the genetic pattern obtained for this species suggests that viruses closely related to RaTG13 may have circulated in the zone comprising southern Yunnan and northern mainland Southeast Asia.In the network of R. malayanus, there are four major haplogroups (named I, II, III and IV in Fig. 3) separated by a minimum of seven mutations. The CO1 haplotypes detected in the localities sampled in southern China (ch2 and ch3) were also found in northern Laos (L1 and L3), suggesting recent gene flow between populations from these two countries. Since a virus related to SARS-CoV-2 (93.7% of genome identity), named RmYN02, was recently isolated from one R. malayanus bat collected in southern Yunnan in June 20195, the genetic pattern obtained for this species suggests that viruses closely related to RmYN02 may have circulated, at least between southern Yunnan and northern Laos. In contrast, the bats sampled in Myanmar were found to be genetically isolated from other geographic populations (haplogroup II in Fig. 3).Two different ecological niches for bat viruses related to either SARS-CoV or SARS-CoV-2In the wild, sarbecoviruses were generally detected after examining fecal samples collected on dozens of bats. For instance, two sarbecoviruses were found7 among the total 59 bats collected at the same cave entrance in northern Cambodia in 2010 (unpublished data). However, this does not mean necessarily that sarbecoviruses were absent in negative samples, as degradation of RNA molecules and very low viral concentrations may prevent the detection of RNA viruses. Despite these difficulties, full genomes of Sarbecovirus have been sequenced from a wide diversity of horseshoe bat species collected in Asia, Africa and Europe5,6,7,8,9,10. Therefore, there is no doubt that Rhinolophus species constitute the natural reservoir host of all sarbecoviruses3,8. The genus Rhinolophus currently includes between 9211 and 10916 insectivorous species that inhabit temperate and tropical regions of the Old World, with a higher biodiversity in Asia (63–68 out of the 92–109 described species) than in Africa (34–38 species), Europe (5 species) and Oceania (5 species). Although some Rhinolophus species are solitary, most of them are gregarious and live in large colonies or small groups generally in caves and hollow trees, but also in burrows, tunnels, abandonned mines, and old buildings11,16. However, they prefer large caves with total darkness, where temperatures are stable and less affected by diurnal and seasonal climatic variations. Importantly, all Rhinolophus species in which sarbecoviruses were detected in previous studies1,5,6,7,8,9,17 are cave species that form small groups or colonies (up to several hundreds)11,18,19.In China, many SCoVrCs were previously detected in several horseshoe bat species, including Rhinolophus sinicus, Rhinolophus ferrumequinum (currently R. nippon)16, Rhinolophus macrotis (currently R. episcopus)16, Rhinolophus pearsoni, and Rhinolophus pusillus, and it has been shown that they circulate not only among conspecific bats from the same colony, but also between bat species inhabiting the same caves17,20,21. The ecological niche predicted for bat SCoVrCs using a data set of 19 points (see online supplementary Table S2) is shown in Fig. 4. The AUC was 0.81. The value was  > 95% CI null-model’s AUCs (0.68), indicating that the model performs significantly better than a random model (see online supplementary Fig. S1). The highest probabilities of occurrence (highlighted in green in Fig. 4) were found in Nepal, Bhutan, Bangladesh, northeastern India, northern Myanmar, northern Vietnam, most regions of China south of the Yellow River, Taiwan, North and South Korea, and southern Japan.Figure 4Ecological niche of bat viruses related to SARS-CoV (SCoVrCs). The geographic distribution of suitable environments was predicted using the Maxent algorithm in ENMTools (see “Methods” section for details). AUC = 0.81. Black circles indicate localities used to build the distribution model (see geographic coordinates in online supplementary Table S2).Full size imageIn Southeast Asia and southern China, SCoV2rCs have currently been found in four Rhinolophus species (R. acuminatus, R. affinis, R. malayanus and R. shameli)1,6,7,8, but the greatest diversity of horseshoe bat species in mainland Southeast Asia (between 28 and 36 species)11,16 suggests that many sarbecoviruses will be discovered soon. Despite the limited data currently available on SCoV2rCs, several arguments support that bat intraspecific and interspecific transmissions also occur with SCoV2rCs. Firslty, recent genomic studies have revealed that SCoV2rCs circulate and evolve among horseshoe bats of the same colony, as five very similar genomes (nucleotide distances between 0.03% and 0.10%) were sequenced from five R. acuminatus bats collected from the same colony in eastern Thailand6, and as two genomes differing at only three nucleotide positions (distance = 0.01%) were sequenced from two R. shameli bats collected at the same cave entrance on the same night7. Secondly, the discovery of four viruses closely related to SARS-CoV-2 (between 96.2 and 91.8% of genome identity) in four different species of Rhinolophus is a strong evidence that interspecific transmission occurred several times in the past. As detailed in online supplementary Table S1, these species were collected together in several localities of Cambodia (three species in C1, C2, and C5; two species in C8), Laos (four species in L10; three species in L9; two species in L1, L5, L8, L11), and Vietnam (two speciess in V10, V9, V17, V18). These data corroborate previous studies suggesting that sarbecoviruses can be transmitted, at least occasionally, between Rhinolophus species sharing the same caves.The ecological niche of bat SCoV2rCs was firstly predicted using the four localities where bat viruses were previously detected1,6,7,8 (Fig. 5a). The highest probabilities of occurrence (highlighted in green in Fig. 5a) were found in Southeast Asia rather than in China. However, the AUC was only 0.58, and the value was  95% CI null-model’s AUCs (0.81), indicating that the model performs significantly better than a random model (see online supplementary Fig. S3). The areas showing the highest probabilities of occurrence (highlighted in green in Fig. 5b) include four main geographic areas: (i) southern Yunnan, northern Laos and bordering regions in northern Thailand and northwestern Vietnam; (ii) southern Laos, southwestern Vietnam, and northeastern Cambodia; (iii) the Cardamom Mountains in southwestern Cambodia and the East region of Thailand; and (iv) the Dawna Range in central Thailand and southeastern Myanmar.Figure 5Ecological niches of bat viruses closely related to SARS-CoV-2 (SCoV2rCs) predicted using 4 points (a) (AUC = 0.58) and 21 points (b) (AUC = 0.96). The geographic distributions of suitable environments were predicted using the Maxent algorithm in ENMTools (see “Methods” section for details). Black circles indicate localities used to build the distribution model (see geographic coordinates in online supplementary Table S1).Full size imageOur results show that bat SCoVrCs and SCoV2rCs have different ecological niches: that of SCoVrCs covers mainly China and several adjacent countries and extends to latitudes between 18° and 43°N, whereas that of SCoV2rCs covers northern mainland Southeast Asia and extends to latitudes between 10° and 24°N. Most Rhinolophus species involved in the ecological niche of SCoVrCs have to hibernate in winter when insect populations become significantly less abundant. This may be different for most Rhinolophus species involved in the ecological niche of SCoVrC2s. Since this ecological difference may be crucial for the dynamics of viral transmission among bat populations, it needs to be further studied through comparative field surveys in different regions of China and Southeast Asia. The ecological niches of SCoVrCs and SCoV2rCs slightly overlap in the zone including southern Yunnan, northern Laos, and northern Vietnam (Figs. 4, 5b). This zone corresponds to the northern edge of tropical monsoon climate23. Highly divergent sarbecoviruses of the two main lineages SCoVrCs and SCoV2rCs are expected to be found in sympatry in this area. This is confirmed by the discovery of both SCoVrCs and SCoV2rCs in horseshoe bats collected in southern Yunnan1,6,21. Collectively, these data suggest that genomic recombination between viruses of the two divergent lineages are more likely to occur in bats roosting, at least seasonally, in the caves of these regions. Since highly recombinant viruses can threaten the benefit of vaccination campaigns, southern Yunnan, northern Laos, and northern Vietnam should be the targets of closer surveillance.Mainland Southeast Asia is the cradle of diversification of bat SCoV2rCsChinese researchers have actively sought sarbecoviruses in all Chinese provinces after the 2002–2004 SARS outbreak. They found many bat SCoVrCs16,20,21 but only two SCoV2rCs1,5 and both of them were discovered in southern Yunnan, the Chinese province bordering Southeast Asia. The ecological niches predicted herein for bat sarbecoviruses suggest that SCoVrCs are dominant in China (Fig. 4) while SCoV2rCs are present mostly in Southeast Asia (Fig. 5). This means that viruses similar to SARS-CoV-2 have been circulating for several decades throughout Southeast Asia, and that different species of bats have exchanged these viruses in the caves they inhabit. The data available on human cases and deaths caused by the COVID-19 pandemic2 indirectly support the hypothesis that the cradle of diversification of bat SCoV2rCs is mainland Southeast Asia, and in particular the areas highlighted in green in Fig. 5b. Indeed, human populations in Cambodia, Laos, Thailand, and Vietnam appear to be much less affected by the COVID-19 pandemic than other countries of the region, such as Indonesia, Malaysia, Myanmar, and the Philippines (Fig. 6). This suggests that some human populations of Cambodia, Laos, Thailand, and Vietnam, in particular rural populations living in contact with wild animals for several generations, have a better immunity against SCoV2rCs because they have been regularly contaminated by bats and/or infected secondary hosts such as pangolins.Figure 6Number of COVID-19 patients per million inhabitants (in blue) and deaths per million inhabitants (in red) for the different countries of Southeast Asia. Data extracted from the Worldometers website2 on June 08, 2021. The figure was drawn in Microsoft Excel and PowerPoint (version 16.16.27).Full size imagePangolins contaminated by bats in Southeast AsiaApart from bats, the Sunda pangolin (Manis javanica) and Chinese pangolin (Manis pentadactyla) are the only wild animals in which viruses related to SARS-CoV-2 have been found so far. However, these discoveries were made in a rather special context, that of pangolin trafficking. Several sick pangolins were seized by Chinese customs in Yunnan province in 2017 (unpublished data), in Guangxi province in 2017–201824 and in Guangdong province in 201925. Even if the viruses sequenced in pangolins are not that close to SARS-CoV-2 (one was 85% identical and the other 90%), they indicate that at least two sarbecoviruses could have been imported into China well before the emergence of COVID-19 epidemic. Indeed, it has been shown that Sunda pangolins collected from different Southeast Asian regions have contaminated each other while in captivity on Chinese territory3. It has been estimated that 43% of seized pangolins were infected by at least one SARS-CoV-2-like virus3. Such a high level of viral prevalence and the symptoms of acute interstitial pneumonia detected in most dead pangolins24 indicate that captive pangolins are highly permissive to infection by SARS-CoV-2-like viruses. The question remained on how the Sunda pangolins became infected initially. Could it have been in their natural Southeast Asian environment, before being captured? The discovery of two new viruses close to SARS-CoV-2 in bats from Cambodia and Thailand7,8 supports this hypothesis, as Rhinolophus bats and pangolins can meet, at least occasionally, in forests of Southeast Asia, possibly in caves, tree hollows or burrows. Further substantiating this hypothesis, the geographic distribution of Manis javanica26 overlaps the ecological niche here predicted for bat SCoV2rCs (Fig. 5), and SARS-CoV-2 neutralizing antibodies have been recently detected in one of the ten pangolin sera sampled from February to July 2020 from three wildlife checkpoint stations in Thailand6. Collectively, these data strengthen the hypothesis that pangolin trafficking is responsible for multiple exports of viruses related to SARS-CoV-2 to China3. More

  • in

    Gene body DNA methylation in seagrasses: inter- and intraspecific differences and interaction with transcriptome plasticity under heat stress

    1.Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14. https://doi.org/10.1111/eva.12137 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Reusch, T. B. Climate change in the oceans: evolutionary versus phenotypically plastic responses of marine animals and plants. Evol. Appl. 7, 104–122. https://doi.org/10.1111/eva.12109 (2014).Article 
    PubMed 

    Google Scholar 
    3.Pazzaglia, J., Reusch, T. B., Terlizzi, A., Marín‐Guirao, L. & Procaccini, G. Phenotypic plasticity under rapid global changes: the intrinsic force for future seagrasses survival. Evol. Appl. (2021).4.Lopez-Maury, L., Marguerat, S. & Baehler, J. Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nat. Rev. Genet. 9, 583–593 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Mäkinen, H., Papakostas, S., Vøllestad, L. A., Leder, E. H. & Primmer, C. R. Plastic and evolutionary gene expression responses are correlated in European grayling (Thymallus thymallus) subpopulations adapted to different thermal environments. J. Hered. 107, 82–89 (2016).PubMed 
    Article 
    CAS 

    Google Scholar 
    6.Alonso, C., Pérez, R., Bazaga, P., Medrano, M. & Herrera, C. M. MSAP markers and global cytosine methylation in plants: a literature survey and comparative analysis for a wild-growing species. Mol. Ecol. Resour. 16, 80–90 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Jeremias, G. et al. Synthesizing the role of epigenetics in the response and adaptation of species to climate change in freshwater ecosystems. Mol. Ecol. 27, 2790–2806 (2018).PubMed 
    Article 

    Google Scholar 
    8.Nicotra, A. B. et al. Adaptive plasticity and epigenetic variation in response to warming in an Alpine plant. Ecol. Evol. 5, 634–647 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Kelly, S., Panhuis, T. & Stoehr, A. (2012).10.Thorson, J. L. et al. Epigenetics and adaptive phenotypic variation between habitats in an asexual snail. Sci. Rep. 7, 1–11 (2017).CAS 
    Article 

    Google Scholar 
    11.Rey, O., Danchin, E., Mirouze, M., Loot, C. & Blanchet, S. Adaptation to global change: a transposable element–epigenetics perspective. Trends Ecol. Evol. 31, 514–526. https://doi.org/10.1016/j.tree.2016.03.013 (2016).Article 
    PubMed 

    Google Scholar 
    12.Law, J. A. & Jacobsen, S. E. Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat. Rev. Genet. 11, 204–220 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Zemach, A., McDaniel, I. E., Silva, P. & Zilberman, D. Genome-wide evolutionary analysis of eukaryotic DNA methylation. Science 328, 916–919 (2010).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    14.Niederhuth, C. E. et al. Widespread natural variation of DNA methylation within angiosperms. Genome Biol. 17, 1–19 (2016).Article 
    CAS 

    Google Scholar 
    15.Zhang, X. et al. Genome-wide high-resolution mapping and functional analysis of DNA methylation in Arabidopsis. Cell 126, 1189–1201 (2006).CAS 
    Article 

    Google Scholar 
    16.Bewick, A. J. et al. On the origin and evolutionary consequences of gene body DNA methylation. Proc. Natl. Acad. Sci. 113, 9111–9116 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Bewick, A. J. & Schmitz, R. J. Gene body DNA methylation in plants. Curr. Opin. Plant Biol. 36, 103–110 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Sarda, S., Zeng, J., Hunt, B. G. & Yi, S. V. The evolution of invertebrate gene body methylation. Mol. Biol. Evol. 29, 1907–1916 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    19.Takuno, S. & Gaut, B. S. Body-methylated genes in Arabidopsis thaliana are functionally important and evolve slowly. Mol. Biol. Evol. 29, 219–227 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    20.Takuno, S. & Gaut, B. S. Gene body methylation is conserved between plant orthologs and is of evolutionary consequence. Proc. Natl. Acad. Sci. 110, 1797–1802 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    21.Takuno, S., Ran, J.-H. & Gaut, B. S. Evolutionary patterns of genic DNA methylation vary across land plants. Nat. Plants 2, 1–7 (2016).Article 
    CAS 

    Google Scholar 
    22.Wendte, J. M. et al. Epimutations are associated with CHROMOMETHYLASE 3-induced de novo DNA methylation. Elife 8, e47891 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Aceituno, F. F., Moseyko, N., Rhee, S. Y. & Gutiérrez, R. A. The rules of gene expression in plants: organ identity and gene body methylation are key factors for regulation of gene expression in Arabidopsis thaliana. BMC Genomics 9, 438 (2008).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    24.Elango, N., Hunt, B. G., Goodisman, M. A. & Soojin, V. Y. DNA methylation is widespread and associated with differential gene expression in castes of the honeybee, Apis mellifera. Proc. Natl. Acad. Sci. 106, 11206–11211 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    25.Gavery, M. R. & Roberts, S. B. DNA methylation patterns provide insight into epigenetic regulation in the Pacific oyster (Crassostrea gigas). BMC Genomics 11, 1–9 (2010).Article 
    CAS 

    Google Scholar 
    26.Zilberman, D., Gehring, M., Tran, R. K., Ballinger, T. & Henikoff, S. Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nat. Genet. 39, 61–69 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Coleman-Derr, D. & Zilberman, D. in Cold Spring Harbor symposia on quantitative biology. 147–154 (Cold Spring Harbor Laboratory Press).28.Kim, M. Y. & Zilberman, D. DNA methylation as a system of plant genomic immunity. Trends Plant Sci. 19, 320–326 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Muyle, A. & Gaut, B. S. Loss of gene body methylation in Eutrema salsugineum is associated with reduced gene expression. Mol. Biol. Evol. 36, 155–158 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    30.Roberts, S. B. & Gavery, M. R. Is there a relationship between DNA methylation and phenotypic plasticity in invertebrates?. Front. Physiol. 2, 116 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Dimond, J. L. & Roberts, S. B. Germline DNA methylation in reef corals: patterns and potential roles in response to environmental change. Mol. Ecol. 25, 1895–1904 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Dixon, G. B., Bay, L. K. & Matz, M. V. Bimodal signatures of germline methylation are linked with gene expression plasticity in the coral Acropora millepora. BMC Genomics 15, 1–11 (2014).Article 
    CAS 

    Google Scholar 
    33.Bird, A. P. DNA methylation and the frequency of CpG in animal DNA. Nucleic Acids Res. 8, 1499–1504 (1980).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Sved, J. & Bird, A. The expected equilibrium of the CpG dinucleotide in vertebrate genomes under a mutation model. Proc. Natl. Acad. Sci. 87, 4692–4696 (1990).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    35.Suzuki, M. M., Kerr, A. R., De Sousa, D. & Bird, A. CpG methylation is targeted to transcription units in an invertebrate genome. Genome Res. 17, 625–631 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Weber, M. et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat. Genet. 39, 457–466 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Glastad, K., Hunt, B. G., Yi, S. & Goodisman, M. DNA methylation in insects: on the brink of the epigenomic era. Insect Mol. Biol. 20, 553–565 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Aliaga, B., Bulla, I., Mouahid, G., Duval, D. & Grunau, C. Universality of the DNA methylation codes in Eucaryotes. Sci. Rep. 9, 1–11 (2019).CAS 
    Article 

    Google Scholar 
    39.Asselman, J., De Coninck, D. I., Pfrender, M. E. & De Schamphelaere, K. A. Gene body methylation patterns in Daphnia are associated with gene family size. Genome Biol Evol 8, 1185–1196 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Park, J. et al. Comparative analyses of DNA methylation and sequence evolution using Nasonia genomes. Mol. Biol. Evol. 28, 3345–3354 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Olsen, J. L. et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nature 530, 331–335. https://doi.org/10.1038/nature16548 (2016).CAS 
    Article 
    PubMed 
    ADS 

    Google Scholar 
    42.Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Chang. 26, 152–158. https://doi.org/10.1016/j.gloenvcha.2014.04.002 (2014).Article 

    Google Scholar 
    43.Nordlund, L. M., Koch, E. W., Barbier, E. B. & Creed, J. C. Correction: Seagrass ecosystem services and their variability across genera and geographical regions. PLoS ONE 12, e0169942 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Orth, R. J. et al. A global crisis for seagrass ecosystems. Bioscience 56, 987–996. https://doi.org/10.1641/0006-3568(2006)56[987:agcfse]2.0.co;2 (2006).Article 

    Google Scholar 
    45.Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl. Acad. Sci. 106, 12377–12381. https://doi.org/10.1073/pnas.0905620106 (2009).Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    46.Koch, M., Bowes, G., Ross, C. & Zhang, X. H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132. https://doi.org/10.1111/j.1365-2486.2012.02791.x (2013).Article 
    ADS 

    Google Scholar 
    47.Marbà, N. & Duarte, C. M. Mediterranean warming triggers seagrass (Posidonia oceanica) shoot mortality. Glob. Change Biol. 16, 2366–2375. https://doi.org/10.1111/j.1365-2486.2009.02130.x (2010).Article 
    ADS 

    Google Scholar 
    48.Thomson, J. A. et al. Extreme temperatures, foundation species, and abrupt ecosystem change: an example from an iconic seagrass ecosystem. Glob. Change Biol. 21, 1463–1474. https://doi.org/10.1111/gcb.12694 (2014).Article 
    ADS 

    Google Scholar 
    49.Maxwell, P. S. et al. Phenotypic plasticity promotes persistence following severe events: physiological and morphological responses of seagrass to flooding. J. Ecol. 102, 54–64 (2014).Article 

    Google Scholar 
    50.Marín-Guirao, L., Ruiz, J. M., Dattolo, E., Garcia-Munoz, R. & Procaccini, G. Physiological and molecular evidence of differential short-term heat tolerance in Mediterranean seagrasses. Sci. Rep. 6, 28615. https://doi.org/10.1038/srep28615 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    51.Sandoval-Gil, J. M., Ruiz, J. M., Marin-Guirao, L., Bernardeau-Esteller, J. & Sanchez-Lizaso, J. L. Ecophysiological plasticity of shallow and deep populations of the Mediterranean seagrasses Posidonia oceanica and Cymodocea nodosa in response to hypersaline stress. Mar. Environ. Res. 95, 39–61. https://doi.org/10.1016/j.marenvres.2013.12.011 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    52.Franssen, S. et al. Transcriptomic resilience to global warming in the seagrass Zostera marina, a marine foundation species. Proc. Natl. Acad. Sci. USA 108, 19276–19281 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    53.Jueterbock, A. et al. Phylogeographic differentiation versus transcriptomic adaptation to warm temperatures in Zostera marina, a globally important seagrass. Mol. Ecol. 25, 5396–5411 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    54.Marín-Guirao, L., Entrambasaguas, L., Dattolo, E., Ruiz, J. M. & Procaccini, G. Molecular mechanisms behind the physiological resistance to intense transient warming in an iconic marine plant. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.01142 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Lee, H. et al. The genome of a southern hemisphere seagrass species (Zostera muelleri). Plant Physiol. (2016).56.Greco, M., Chiappetta, A., Bruno, L. & Bitonti, M. B. Effects of light deficiency on genome methylation in Posidonia oceanica. Mar. Ecol. Prog. Ser. 473, 103–114 (2013).CAS 
    Article 
    ADS 

    Google Scholar 
    57.Greco, M., Chiappetta, A., Bruno, L. & Bitonti, M. B. In Posidonia oceanica cadmium induces changes in DNA methylation and chromatin patterning. J. Exp. Bot. 63, 695–709. https://doi.org/10.1093/jxb/err313 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Ruocco, M., De Luca, P., Marín-Guirao, L. & Procaccini, G. Differential leaf age-dependent thermal plasticity in the keystone seagrass Posidonia oceanica. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01556 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Ruocco, M., Marín-Guirao, L. & Procaccini, G. Within- and among-leaf variations in photo-physiological functions, gene expression and DNA methylation patterns in the large-sized seagrass Posidonia oceanica. Mar. Biol. 166, 24. https://doi.org/10.1007/s00227-019-3482-8 (2019).CAS 
    Article 

    Google Scholar 
    60.Ruocco, M. et al. A king and vassals’ tale: Molecular signatures of clonal integration in Posidonia oceanica under chronic light shortage. J. Ecol. (2020).61.Jueterbock, A. et al. The seagrass methylome is associated with variation in photosynthetic performance among clonal shoots. Front. Plant Sci. 11 (2020).62.Marín-Guirao, L. et al. Carbon economy of Mediterranean seagrasses in response to thermal stress. Mar. Pollut. Bull. 135, 617–629 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    63.Beca-Carretero, P. et al. Effects of an experimental heat wave on fatty acid composition in two Mediterranean seagrass species. Mar. Pollut. Bull. 134, 27–37 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    64.Angers, B., Castonguay, E. & Massicotte, R. Environmentally induced phenotypes and DNA methylation: how to deal with unpredictable conditions until the next generation and after. Mol. Ecol. 19, 1283–1295 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    65.Dubin, M. J. et al. DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation. Elife 4, e05255 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Kawakatsu, T. et al. Epigenomic diversity in a global collection of Arabidopsis thaliana accessions. Cell 166, 492–505 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Serres-Giardi, L., Belkhir, K., David, J. & Glémin, S. Patterns and evolution of nucleotide landscapes in seed plants. Plant Cell 24, 1379–1397 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    69.Tatarinova, T., Elhaik, E. & Pellegrini, M. Cross-species analysis of genic GC3 content and DNA methylation patterns. Genome Biol. Evol. 5, 1443–1456 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Vining, K. J. et al. Dynamic DNA cytosine methylation in the Populus trichocarpa genome: tissue-level variation and relationship to gene expression. BMC Genomics 13, 27 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Lyko, F. et al. The honey bee epigenomes: differential methylation of brain DNA in queens and workers. PLoS Biol 8, 1506 (2010).Article 
    CAS 

    Google Scholar 
    72.Cortijo, S., Aydin, Z., Ahnert, S. & Locke, J. C. Widespread inter-individual gene expression variability in Arabidopsis thaliana. Mol. Syst. Biol. 15, e8591 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Procaccini, G., Olsen, J. L. & Reusch, T. B. H. Contribution of genetics and genomics to seagrass biology and conservation. J. Exp. Mar. Biol. Ecol. 350, 234–259. https://doi.org/10.1016/j.jembe.2007.05.035 (2007).CAS 
    Article 

    Google Scholar 
    74.Alberto, F. et al. Genetic differentiation and secondary contact zone in the seagrass Cymodocea nodosa across the Mediterranean-Atlantic transition region. J. Biogeogr. 35, 1279–1294 (2008).Article 

    Google Scholar 
    75.Becker, C. et al. Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature 480, 245–249 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    76.Schmitz, R. J. et al. Patterns of population epigenomic diversity. Nature 495, 193–198 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    77.Yi, S. V. Insights into epigenome evolution from animal and plant methylomes. Genome Biol. Evol. 9, 3189–3201 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Jahnke, M. et al. Adaptive responses along a depth and a latitudinal gradient in the endemic seagrass Posidonia oceanica. Heredity https://doi.org/10.1038/s41437-018-0103-0 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Tuya, F. et al. Biogeographical scenarios modulate seagrass resistance to small-scale perturbations. J. Ecol. 107, 1263–1275 (2019).Article 

    Google Scholar 
    80.Gao, G. et al. Comparison of the heat stress induced variations in DNA methylation between heat-tolerant and heat-sensitive rapeseed seedlings. Breed. Sci. 64, 125–133 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    81.Dowen, R. H. et al. Widespread dynamic DNA methylation in response to biotic stress. Proc. Natl. Acad. Sci. 109, E2183–E2191 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Wada, Y., Miyamoto, K., Kusano, T. & Sano, H. Association between up-regulation of stress-responsive genes and hypomethylation of genomic DNA in tobacco plants. Mol. Genet. Genomics 271, 658–666 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    83.Yaish, M. W., Colasanti, J. & Rothstein, S. J. The role of epigenetic processes in controlling flowering time in plants exposed to stress. J. Exp. Bot. 62, 3727–3735 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    84.Secco, D. et al. Stress induced gene expression drives transient DNA methylation changes at adjacent repetitive elements. Elife 4, e09343 (2015).PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    85.Marín-Guirao, L., Entrambasaguas, L., Ruiz, J. M. & Procaccini, G. Heat-stress induced flowering can be a potential adaptive response to ocean warming for the iconic seagrass Posidonia oceanica. Mol. Ecol. 28, 2486–2501. https://doi.org/10.1111/mec.15089 (2019).Article 
    PubMed 

    Google Scholar 
    86.Nguyen, H. M. et al. Stress memory in seagrasses: first insight into the effects of thermal priming and the role of epigenetic modifications. Front. Plant Sci. 11, 494 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Pikaard, C. S. & Scheid, O. M. Epigenetic regulation in plants. Cold Spring Harbor Perspect. Biol. 6, a019315 (2014).Article 
    CAS 

    Google Scholar 
    88.Yu, Y. et al. Cytosine methylation alteration in natural populations of Leymus chinensis induced by multiple abiotic stresses. PLoS ONE 8, e55772 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    89.Liu, R. & Lang, Z. The mechanism and function of active DNA demethylation in plants. J. Integr. Plant. Biol. 62, 148–159 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    90.Xu, X. et al. A CRISPR-based approach for targeted DNA demethylation. Cell Discovery 2, 1–12 (2016).
    Google Scholar 
    91.Arnaud-Haond, S. et al. Implications of extreme life span in clonal organisms: millenary clones in meadows of the threatened seagrass Posidonia oceanica. PLoS ONE 7, e30454. https://doi.org/10.1371/journal.pone.0030454 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    92.Mascaró, O., Romero, J. & Pérez, M. Seasonal uncoupling of demographic processes in a marine clonal plant. Estuar. Coast. Shelf Sci. 142, 23–31 (2014).Article 
    ADS 

    Google Scholar 
    93.Olesen, B., Enríquez, S., Duarte, C. M. & Sand-Jensen, K. Depth-acclimation of photosynthesis, morphology and demography of Posidonia oceanica and Cymodocea nodosa in the Spanish Mediterranean Sea. Mar. Ecol. Prog. Ser. 236, 89–97. https://doi.org/10.3354/meps236089 (2002).Article 
    ADS 

    Google Scholar 
    94.Ruocco, M. et al. Genomewide transcriptional reprogramming in the seagrass Cymodocea nodosa under experimental ocean acidification. Mol. Ecol. 26, 4241–4259. https://doi.org/10.1111/mec.14204 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    95.Fraley, C. & Raftery, A. E. Model-based methods of classification: using the mclust software in chemometrics. J. Stat. Softw. 18, 1–13 (2007).Article 

    Google Scholar 
    96.R Core Team (ISBN 3-900051-07-0, 2012).97.Benaglia, T., Chauveau, D., Hunter, D., Young, D. mixtools: an R package for analyzing finite mixture models (2009).98.Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

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

    Google Scholar 
    100.Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. https://doi.org/10.1093/bioinformatics/btp616 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Wet-dry cycles protect surface-colonizing bacteria from major antibiotic classes

    1.Or D, Smets BF, Wraith J, Dechesne A, Friedman S. Physical constraints affecting bacterial habitats and activity in unsaturated porous media–a review. Adv Water Resour. 2007;30:1505–27.Article 

    Google Scholar 
    2.Burkhardt J, Hunsche M. “Breath figures” on leaf surfaces—formation and effects of microscopic leaf wetness. Front plant Sci. 2013;4:422.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Wolf AB, Vos M, de Boer W, Kowalchuk GA. Impact of matric potential and pore size distribution on growth dynamics of filamentous and non-filamentous soil bacteria. PloS One. 2013;8:e83661.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    4.Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MO, Dantas G. The shared antibiotic resistome of soil bacteria and human pathogens. Science. 2012;337:1107–11.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Williams S, Vickers J. The ecology of antibiotic production. Microb Ecol. 1986;12:43–52.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Raaijmakers JM, Weller DM, Thomashow LS. Frequency of antibiotic-producing Pseudomonas spp. in natural environments. Appl Environ Microbiol. 1997;63:881–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Wells JS, Hunter JC, Astle GL, Sherwood JC, Ricca cM, Trejo WH, et al. Distribution of β-lactam and β-lactone producing bacteria in nature. The. J Antibiot. 1982;35:814–21.CAS 
    Article 

    Google Scholar 
    8.Kinkel LL, Schlatter DC, Xiao K, Baines AD. Sympatric inhibition and niche differentiation suggest alternative coevolutionary trajectories among Streptomycetes. ISME J. 2014;8:249–56.CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Vetsigian K, Jajoo R, Kishony R. Structure and evolution of Streptomyces interaction networks in soil and in silico. PLoS Biol. 2011;9:e1001184.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Traxler MF, Kolter R. Natural products in soil microbe interactions and evolution. Nat Prod Rep. 2015;32:956–70.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Franklin AM, Aga DS, Cytryn E, Durso LM, McLain JE, Pruden A, et al. Antibiotics in agroecosystems: introduction to the special section. J Environ Qual. 2016;45:377–93.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Jechalke S, Heuer H, Siemens J, Amelung W, Smalla K. Fate and effects of veterinary antibiotics in soil. Trends Microbiol. 2014;22:536–45.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Mompelat S, Le Bot B, Thomas O. Occurrence and fate of pharmaceutical products and by-products, from resource to drinking water. Environ Int. 2009;35:803–14.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Kelsic ED, Zhao J, Vetsigian K, Kishony R. Counteraction of antibiotic production and degradation stabilizes microbial communities. Nature. 2015;521:516–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Cordero OX, Wildschutte H, Kirkup B, Proehl S, Ngo L, Hussain F, et al. Ecological populations of bacteria act as socially cohesive units of antibiotic production and resistance. Science. 2012;337:1228–31.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Schlatter DC, Song Z, Vaz-Jauri P, Kinkel LL. Inhibitory interaction networks among coevolved Streptomyces populations from prairie soils. Plos One. 2019;14:e0223779.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Abrudan MI, Smakman F, Grimbergen AJ, Westhoff S, Miller EL, Van Wezel GP, et al. Socially mediated induction and suppression of antibiosis during bacterial coexistence. Proc Natl Acad Sci. 2015;112:11054–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Brauner A, Fridman O, Gefen O, Balaban NQ. Distinguishing between resistance, tolerance and persistence to antibiotic treatment. Nat Rev Microbiol. 2016;14:320.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Andersson DI, Levin BR. The biological cost of antibiotic resistance. Curr Opin Microbiol. 1999;2:489–93.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Handwerger S, Tomasz A. Antibiotic tolerance among clinical isolates of bacteria. Annu Rev Pharmacol Toxicol. 1985;25:349–80.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Kester JC, Fortune SM. Persisters and beyond: mechanisms of phenotypic drug resistance and drug tolerance in bacteria. Crit Rev Biochem Mol Biol. 2014;49:91–101.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Wood KB, Cluzel P. Trade-offs between drug toxicity and benefit in the multi-antibiotic resistance system underlie optimal growth of E. coli. BMC Syst Biol. 2012;6:1–11.Article 

    Google Scholar 
    23.Nguyen D, Joshi-Datar A, Lepine F, Bauerle E, Olakanmi O, Beer K, et al. Active starvation responses mediate antibiotic tolerance in biofilms and nutrient-limited bacteria. Science. 2011;334:982–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Meredith HR, Srimani JK, Lee AJ, Lopatkin AJ, You L. Collective antibiotic tolerance: mechanisms, dynamics and intervention. Nat Chem Biol. 2015;11:182.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Nagarajan R, Boeck LD, Gorman M, Hamill RL, Higgens CE, Hoehn MM, et al. beta.-Lactam antibiotics from Streptomyces. J Am Chem Soc. 1971;93:2308–10.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Imada A, Kitano K, Kintaka K, Muroi M, Asai M. Sulfazecin and isosulfazecin, novel β-lactam antibiotics of bacterial origin. Nature. 1981;289:590–1.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Sykes R, Cimarusti C, Bonner D, Bush K, Floyd D, Georgopapadakou N, et al. Monocyclic β-lactam antibiotics produced by bacteria. Nature. 1981;291:489.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Wells JS, TREJO WH, PRINCIPE PA, Bush K, Georgopapadakou N, Bonner DP, et al. EM5400, a family of monobactam antibiotics produced by Agrobacterium radiobacter. J Antibiot. 1982;35:295–9.CAS 
    Article 

    Google Scholar 
    29.ThaKurIa B, Lahon K. The beta lactam antibiotics as an empirical therapy in a developing country: An update on their current status and recommendations to counter the resistance against them. J Clin Diagn Res. 2013;7:1207.PubMed 
    PubMed Central 

    Google Scholar 
    30.Russ D, Glaser F, Tamar ES, Yelin I, Baym M, Kelsic ED, et al. Escape mutations circumvent a tradeoff between resistance to a beta-lactam and resistance to a beta-lactamase inhibitor. Nat Commun. 2020;11:1–9.Article 
    CAS 

    Google Scholar 
    31.Grinberg M, Orevi T, Steinberg S, Kashtan N. Bacterial survival in microscopic surface wetness. eLife. 2019;8:e48508.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Orevi T, Kashtan N. Life in a droplet: microbial ecology in microscopic surface wetness. Front Microbiol. 2021;12:797.Article 

    Google Scholar 
    33.Mauer LJ, Taylor LS. Water-solids interactions: deliquescence. Annu Rev food Sci Technol. 2010;1:41–63.CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Wise ME, Martin ST, Russell LM, Buseck PR. Water uptake by NaCl particles prior to deliquescence and the phase rule. Aerosol Sci Technol. 2008;42:281–94.CAS 
    Article 

    Google Scholar 
    35.Burkhardt J, Koch K, Kaiser H. Deliquescence of deposited atmospheric particles on leaf surfaces. J Water, Air Soil Pollut: Focus. 2001;1:313–21.CAS 
    Article 

    Google Scholar 
    36.Beattie GA. Water relations in the Interaction of foliar bacterial pathogens with plants. Annu Rev Phytopathol. 2011;49:533–55.CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Davila AF, Hawes I, Ascaso C, Wierzchos J. Salt deliquescence drives photosynthesis in the hyperarid A tacama D esert. Environ Microbiol Rep. 2013;5:583–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Dai S, Shin H, Santamarina JC. Formation and development of salt crusts on soil surfaces. Acta Geotechnica. 2016;11:1103–9.Article 

    Google Scholar 
    39.Trechsel HR. Moisture control in buildings. ASTM International; West Conshohocken, PA19428-2959, USA; 1994.40.Schwartz-Narbonne H, Donaldson DJ. Water uptake by indoor surface films. Sci Rep. 2019;9:1–10.CAS 
    Article 

    Google Scholar 
    41.Patrick D, Findon G, Miller T. Residual moisture determines the level of touch-contact-associated bacterial transfer following hand washing. Epidemiol Infect. 1997;119:319–25.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Tang IN, Munkelwitz HR. Composition and temperature dependence of the deliquescence properties of hygroscopic aerosols. Atmos Environ Part A Gen Top. 1993;27:467–73.Article 

    Google Scholar 
    43.Pöschl U. Atmospheric aerosols: composition, transformation, climate and health effects. Angew Chem Int Ed. 2005;44:7520–40.Article 
    CAS 

    Google Scholar 
    44.Tecon R. Bacterial survival: life on a leaf. eLife. 2019;8:e52123.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Vejerano EP, Marr LC. Physico-chemical characteristics of evaporating respiratory fluid droplets. J R Soc Interface. 2018;15:20170939.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    46.Rubasinghege G, Grassian VH. Role (s) of adsorbed water in the surface chemistry of environmental interfaces. Chem Commun. 2013;49:3071–94.CAS 
    Article 

    Google Scholar 
    47.Campbell TD, Febrian R, McCarthy JT, Kleinschmidt HE, Forsythe JG, Bracher PJ. Prebiotic condensation through wet–dry cycling regulated by deliquescence. Nat Commun. 2019;10:1–7.Article 
    CAS 

    Google Scholar 
    48.Alsved M, Holm S, Christiansen S, Smidt M, Rosati B, Ling M, et al. Effect of aerosolization and drying on the viability of pseudomonas syringae cells. Front Microbiol. 2018;9:3086.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Xie X, Li Y, Zhang T, Fang HH. Bacterial survival in evaporating deposited droplets on a teflon-coated surface. Appl Microbiol Biotechnol. 2006;73:703–12.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Runkel S, Wells HC, Rowley G. Living with stress: a lesson from the enteric pathogen Salmonella enterica. Adv Appl Microbiol. 2013;83:87–144.51.Amaeze N, Akinbobola A, Chukwuemeka V, Abalkhaila A, Ramage G, Kean R, et al. Development of a high throughput and low cost model for the study of semi-dry biofilms. Biofouling. 2020:36:403–15.52.Tuomanen E, Cozens R, Tosch W, Zak O, Tomasz A. The rate of killing of Escherichia coli byβ-lactam antibiotics is strictly proportional to the rate of bacterial growth. Microbiology. 1986;132:1297–304.CAS 
    Article 

    Google Scholar 
    53.Eng R, Padberg F, Smith S, Tan E, Cherubin C. Bactericidal effects of antibiotics on slowly growing and nongrowing bacteria. Antimicrobial Agents Chemother. 1991;35:1824–8.CAS 
    Article 

    Google Scholar 
    54.Lee S, Foley E, Epstein JA. Mode of action of penicillin: I. Bacterial growth and penicillin activity—Staphylococcus aureus FDA. J Bacteriol. 1944;48:393.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Lopatkin AJ, Stokes JM, Zheng EJ, Yang JH, Takahashi MK, You L, et al. Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate. Nat Microbiol. 2019;4:2109–17.56.Yoon H, Park B-Y, Oh M-H, Choi K-H, Yoon Y. Effect of NaCl on heat resistance, antibiotic susceptibility, and Caco-2 cell invasion of Salmonella. BioMed Res Int. 2013;2013:274096.57.Zhu M, Dai X. High salt cross-protects Escherichia coli from antibiotic treatment through increasing efflux pump expression. mSphere 3: e00095-18. mSphere. 2018;3:e00095–18.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Lee AJ, Wang S, Meredith HR, Zhuang B, Dai Z, You L. Robust, linear correlations between growth rates and β-lactam–mediated lysis rates. Proc Natl Acad Sci. 2018;115:4069–74.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Loftin KA, Adams CD, Meyer MT, Surampalli R. Effects of ionic strength, temperature, and pH on degradation of selected antibiotics. J Environ Qual. 2008;37:378–86.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Thonus IP, Fontijne P, Michel MF. Ampicillin susceptibility and ampicillin-induced killing rate of Escherichia coli. Antimicrobial Agents Chemother. 1982;22:386–90.CAS 
    Article 

    Google Scholar 
    61.Cho H, Uehara T, Bernhardt TG. Beta-lactam antibiotics induce a lethal malfunctioning of the bacterial cell wall synthesis machinery. Cell. 2014;159:1300–11.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Yao Z, Kahne D, Kishony R. Distinct single-cell morphological dynamics under beta-lactam antibiotics. Mol Cell. 2012;48:705–12.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Battesti A, Majdalani N, Gottesman S. The RpoS-mediated general stress response in Escherichia coli. Annu Rev Microbiol. 2011;65:189–213.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Bernier SP, Lebeaux D, DeFrancesco AS, Valomon A, Soubigou G, Coppée J-Y, et al. Starvation, together with the SOS response, mediates high biofilm-specific tolerance to the fluoroquinolone ofloxacin. PLoS Genet. 2013;9:e1003144.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Pu Y, Zhao Z, Li Y, Zou J, Ma Q, Zhao Y, et al. Enhanced efflux activity facilitates drug tolerance in dormant bacterial cells. Mol Cell. 2016;62:284–94.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Martins D, McKay G, Sampathkumar G, Khakimova M, English AM, Nguyen D. Superoxide dismutase activity confers (p) ppGpp-mediated antibiotic tolerance to stationary-phase Pseudomonas aeruginosa. Proc Natl Acad Sci. 2018;115:9797–802.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Page R, Peti W. Toxin-antitoxin systems in bacterial growth arrest and persistence. Nat Chem Biol. 2016;12:208–14.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Liao X, Ma Y, Daliri EB-M, Koseki S, Wei S, Liu D, et al. Interplay of antibiotic resistance and food-associated stress tolerance in foodborne pathogens. Trends Food Sci Technol. 2020;95:97–106.CAS 
    Article 

    Google Scholar 
    69.Levin-Reisman I, Brauner A, Ronin I, Balaban NQ. Epistasis between antibiotic tolerance, persistence, and resistance mutations. Proc Natl Acad Sci. 2019;116:14734–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N, Balaban NQ. Antibiotic tolerance facilitates the evolution of resistance. Science. 2017;355:826–30.CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Year-round high abundances of the world’s smallest marine vertebrate (Schindleria) in the Red Sea and worldwide associations with lunar phases

    1.Giltay, L. Les larves de Schindler sont-elles des Hemirhamphidae?. Notes Ichthyol. Mus. Roy. d’Hist. Nat Belgique 10, 1–10 (1934).
    Google Scholar 
    2.Johnson, G. D. & Brothers, E. B. Schindleria: a paedomorphic goby (Teleostei: Gobioidei). Bull. Mar. Sci. 52, 441–471 (1993).
    Google Scholar 
    3.Kon, T. & Yoshino, T. Diversity and evolution of life histories of gobioid fishes from the viewpoint of heterochrony. Mar. Freshw. Res. 53, 377–402 (2002).Article 

    Google Scholar 
    4.Randall, J. E. & Cea, A. Shore fishes of Easter Island. (University of Hawaii Press, 2011).5.Kon, T., Yoshino, T., Mukai, T. & Nishida, M. DNA sequences identify numerous cryptic species of the vertebrate: a lesson from the gobioid fish Schindleria. Mol. Phylogenet. Evol. 44, 53–62 (2007).CAS 
    Article 

    Google Scholar 
    6.Robitzch, V., Schröder, M. & Ahnelt, H. Morphometrics reveal inter- and intraspecific sexual dimorphisms in two Hawaiian Schindleria, the long dorsal finned S. praematura and the short dorsal finned S. pietschmanni. Zool. Anz. 292, 197–206 (2021).Article 

    Google Scholar 
    7.Schindler, O. Ein neuer Hemirhamphus aus dem Pazifischen Ozean. Anzeiger der Akad. der Wissenschaften Wien 67, 79–80 (1930).
    Google Scholar 
    8.Schindler, O. Sexually mature larval Hemiramphidae from the Hawaiian Islands. Bull. Bernice P. Bish. Museum 1–28 (1932).9.Landaeta, M. F., Veas, R. & Castro, L. R. First record of the paedomorphic goby Schindleria praematura, Easter Island, South Pacific. J. Fish Biol. 61, 289–292 (2002).Article 

    Google Scholar 
    10.Watson, W. & Walker, H. J. J. The world’s smallest vertebrate, Schindleria brevipinguis, a new paedomorphic species in the family Schindleriidae (Perciformes: Gobioidei). Rec. Aust. Museum 56, 139–142 (2004).Article 

    Google Scholar 
    11.Kon, T., Yoshino, T. & Nishida, M. Cryptic species of the gobioid paedomorphic genus Schindleria from Palau, Western Pacific Ocean. Ichthyol. Res. https://doi.org/10.1007/s10228-010-0178-y (2010).Article 

    Google Scholar 
    12.Ahnelt, H. & Sauberer, M. Deep-water, offshore, and new records of Schindler’s fishes, Schindleria (Teleostei, Gobiidae), from the Indo-west Pacific collected during the Dana-Expedition, 1928–1930. Zootaxa 4731, 451–470 (2020).Article 

    Google Scholar 
    13.Bruun, A. F. A study of a collection of the fish Schindleria from South Pacific waters. Dana Rep. 21, 1–12 (1940).
    Google Scholar 
    14.Jones, S. & Kumaran, M. On the fishes of the genus Schindleria (Giltay) from the Indian Ocean. J. Mar. Biol. 6, 257–264 (1964).
    Google Scholar 
    15.Leis, J. M. Coral Sea atoll lagoons: closed nurseries for the larvae of a few coral reef fishes. Bull. Mar. Sci. 54, 206–227 (1994).ADS 

    Google Scholar 
    16.Belyanina, T. P. Ichthyoplankton in the regions of the Nazca and Salas y Gomez submarine ridges. J. Ichthyol. 29, 84–90 (1989).
    Google Scholar 
    17.Parin, N. V., Mironov, A. N. & Nesis, K. N. Biology of the Nazca and Salas y Gomez submarine ridges, an outpost of the Indo-West Pacific fauna in the Eastern Pacific Ocean: composition and distribution of the fauna, its communities and history. Adv. Mar. Biol. 32, 147–242 (1997).
    Google Scholar 
    18.Ahnelt, H. & Sauberer, M. A new species of Schindler’s fish (Teleostei: Gobiidae: Schindleria) from the Malay archipelago (Southeast Asia), with notes on the caudal fin complex of Schindleria. Zootaxa 4531, 95–108 (2018).Article 

    Google Scholar 
    19.Leis, J. M., Goldman, B. & Read, S. E. Epibenthic fish larvae in the Great Barrier Reef Lagoon near Lizard Island, Australia. Japanese J. Ichthyol. 35, 428–433 (1989).
    Google Scholar 
    20.Thacker, C. & Grier, H. Unusual gonad structure in the paedomorphic teleost Schindleria praematura (Teleostei Gobioidei): a comparison with other gobioid fishes. J. Fish Biol. 66, 378–391 (2005).Article 

    Google Scholar 
    21.Young, S.-S. & Chiu, T.-S. New records of a paedomorphic fish Schindleria praematura (Pisces: Schindleriidae), from Waters of Taiwan. Acta Zool. Taiwanica 11, 127–137 (2000).
    Google Scholar 
    22.Watson, W. & Leis, J. M. Ichthyoplankton of Kaneohe Bay, Hawaii. A one-year study of fish eggs and larvae. 1–178 (University of Hawaiʻi Sea Grant Program, 1974).23.Leis, J. M. & Trnski, T. The larvae of Indo-Pacific shorefishes. (New South Wales Univ. Press, Sydney & Univ. of Hawaii Press, 1989).24.Fricke, R. & Abu El-Regal, M. A. Schindleria nigropunctata, a new species of paedomorphic gobioid fish from the Red Sea (Teleostei: Schindleriidae). Mar. Biodivers. https://doi.org/10.1007/s12526-017-0831-z (2017).Article 

    Google Scholar 
    25.Fricke, R. & Abu El-Regal, M. A. Schindleria elongata, a new species of paedomorphic gobioid from the Red Sea (Teleostei: Schindleriidae). J Fish Biol 2, 1–8. https://doi.org/10.1111/jfb.13280 (2017).Article 

    Google Scholar 
    26.Abu El-Regal, M. A. & Kon, T. First record of the Schindler’s fish, Schindleria praematura (Actinopterygii: Perciformes: Schindleriidae), from the Red Sea. Acta Ichthyol. Piscat. 49, 75–78 (2019).Article 

    Google Scholar 
    27.EAbu El-Regal, M. & Kon, T. First record of the paedomorphic fish Schindleria (Gobioidei, Schindleriidae) from the Red Sea. J. Fish Biol. 72, 1539–1543 (2008).Article 

    Google Scholar 
    28.Ahnelt, H. Redescription of the paedomorphic goby Schindleria nigropunctata Fricke & El-Regal 2017 (Teleostei: Gobiidae) from the Red Sea. Zootaxa 4615, 450–456 (2019).Article 

    Google Scholar 
    29.Contreras, J. E., Landaeta, M. F., Plaza, G., Ojeda, F. P. & Bustos, C. A. The contrasting hatching patterns and larval growth of two sympatric clingfishes inferred by otolith microstructure analysis. Mar. Freshw. Res. 64, 157–167 (2013).Article 

    Google Scholar 
    30.Team, R. C. R: a language and environment for statistical computing (version 3.6). https://www.R-project.org (2020).31.Kleiber, C. & Zeileis, A. Applied econometrics with R. (Springer Science & Business Media, 2008).32.Kleiber, C. & Zeileis, A. AER: applied econometrics with R. R package version 1.1. (2009).33.Batschelet, E. Circular statistics in biology. (Academic Press, New York, 1981).34.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 
    35.Robitzch, V. & Berumen, M. L. Recruitment of coral reef fishes along a cross-shelf gradient in the Red Sea peaks outside the hottest season. Coral Reefs 39, 1565–1579 (2020).Article 

    Google Scholar 
    36.Whittle, A. G. Ecology, abundance, diversity, and distribution of larval fishes and Schindleriidae (Teleostei: Gobioidei) at two sites on O’ahu, Hawai’i. (University of Hawaiʻi, 2003).37.Depczynski, M. & Bellwood, D. R. Shortest recorded vertebrate lifespan found in a coral reef fish. Curr. Biol. 15, 10 (2005).Article 

    Google Scholar 
    38.Isari, S. et al. Exploring the larval fish community of the central Red Sea with an integrated morphological and molecular approach. PLoS ONE 12, 1–24 (2017).Article 

    Google Scholar 
    39.Depczynski, M. & Bellwood, D. R. Extremes, plasticity, and invariance in vertebrate life history traits: insights from coral reef fishes. Ecology 87, 3119–3127 (2006).Article 

    Google Scholar 
    40.Nanninga, G. B., Saenz-Agudelo, P., Zhan, P., Hoteit, I. & Berumen, M. L. Not finding Nemo: limited reef-scale retention in a coral reef fish. Coral Reefs 34, 383–392 (2015).ADS 
    Article 

    Google Scholar 
    41.Hernaman, V. & Munday, P. L. Life-history characteristics of coral reef gobies. I. Growth and life-span. Mar. Ecol. Prog. Ser. 290, 207–221 (2005).ADS 
    Article 

    Google Scholar 
    42.Lefèvre, C. D., Nash, K. L., González-Cabello, A. & Bellwood, D. R. Consequences of extreme life history traits on population persistence: do short-lived gobies face demographic bottlenecks?. Coral Reefs 35, 399–409 (2016).ADS 
    Article 

    Google Scholar  More

  • in

    Tipping point realized in cod fishery

    1.Heinze, C. et al. The quiet crossing of ocean tipping points. Proc. Natl. Acad. Sci. 118, e2008478118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Dakos, V. et al. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 3, 355–362 (2019).PubMed 
    Article 

    Google Scholar 
    3.Myers, R., Hutchings, J. & Barrowman, N. Hypotheses for the decline of cod in the North Atlantic. Mar. Ecol. Prog. Ser. 138, 293–308 (1996).ADS 
    Article 

    Google Scholar 
    4.Sguotti, C. et al. Catastrophic dynamics limit Atlantic cod recovery. Proc. R. Soc. B Biol. Sci. 286, 20182877 (2019).Article 

    Google Scholar 
    5.Levin, P. S. & Möllmann, C. Marine ecosystem regime shifts: Challenges and opportunities for ecosystem-based management. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130275 (2015).Article 

    Google Scholar 
    6.King, J. R., Mcfarlane, G. A. & Punt, A. E. Shifts in fisheries management: Adapting to regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130277 (2015).Article 

    Google Scholar 
    7.Döring, R., Berkenhagen, J., Hentsch, S. & Kraus, G. Small-Scale Fisheries in Germany: A Disappearing Profession? In Small-Scale Fisheries in Europe: Status, Resilience and Governance (eds. Pascual-Fernández, J. J., Pita, C. & Bavinck, M.) vol. 23 483–502 (Springer International Publishing, 2020).8.Papaioannou, E. A., Vafeidis, A. T., Quaas, M. F., Schmidt, J. O. & Strehlow, H. V. Using indicators based on primary fisheries’ data for assessing the development of the German Baltic small-scale fishery and reviewing its adaptation potential to changes in resource abundance and management during 2000–09. Ocean Coast. Manag. 98, 38–50 (2014).Article 

    Google Scholar 
    9.EU. Regulation (EU) 2016/1139 of the European Parliament and of the Council of 6 July 2016 establishing a multiannual plan for the stocks of cod, herring and sprat in the Baltic Sea and the fisheries exploiting those stocks, amending Council Regulation (EC) No 2187/2005 and repealing Council Regulation (EC) No 1098/2007. (2016).10.Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Lenton, T. M. Environmental tipping points. Annu. Rev. Environ. Resour. 38, 1–29 (2013).ADS 
    Article 

    Google Scholar 
    12.Möllmann, C., Folke, C., Edwards, M. & Conversi, A. Marine regime shifts around the globe: Theory, drivers and impacts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130260 (2015).Article 

    Google Scholar 
    13.ICES. Advice cod in subdivisions 22–24, western Baltic stock (western Baltic Sea). (2019) https://doi.org/10.17895/ICES.ADVICE.5587.14.Conversi, A. et al. A holistic view of marine regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130279 (2015).Article 

    Google Scholar 
    15.Ratajczak, Z. et al. Abrupt change in ecological systems: Inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).PubMed 
    Article 

    Google Scholar 
    16.Turner, M. G. et al. Climate change, ecosystems and abrupt change: Science priorities. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190105 (2020).Article 

    Google Scholar 
    17.Scheffer, M. & Carpenter, S. R. Catastrophic regime shifts in ecosystems: Linking theory to observation. Trends Ecol. Evol. 18, 648–656 (2003).Article 

    Google Scholar 
    18.Beisner, B., Haydon, D. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Environ. 1, 376–382 (2003).Article 

    Google Scholar 
    19.Subbey, S., Devine, J. A., Schaarschmidt, U. & Nash, R. D. Modelling and forecasting stock–recruitment: Current and future perspectives. ICES J. Mar. Sci. 71, 2307–2322 (2014).Article 

    Google Scholar 
    20.Grasman, R. P. P. P., Maas, H. L. J. van der & Wagenmakers, E.-J. Fitting the Cusp Catastrophe in r : A cusp Package Primer. J. Stat. Softw. 32, 1-27 (2009).21.Thom, R. Structural Stability and Morphogenesis—An Outline of a General Theory of Models (Benjamin Inc, 1975).MATH 

    Google Scholar 
    22.Zeeman, E. Catastrophe theory. Sci. Am. 234, 65–83 (1976).Article 

    Google Scholar 
    23.Barunik, J. & Vosvrda, M. Can a stochastic cusp catastrophe model explain stock market crashes?. J. Econ. Dyn. Control 33, 1824–1836 (2009).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    24.Xiaoping, Z., Jiahui, S. & Yuan, C. Analysis of crowd jam in public buildings based on cusp-catastrophe theory. Build. Environ. 45, 1755–1761 (2010).Article 

    Google Scholar 
    25.Guastello, S. J., Boeh, H., Shumaker, C. & Schimmels, M. Catastrophe models for cognitive workload and fatigue. Theor. Issues Ergon. Sci. 13, 586–602 (2012).Article 

    Google Scholar 
    26.Angelis, V., Angelis-Dimakis, A. & Dimaki, K. The Cusp Catastrophe model in describing a bank’s attractiveness as measured by its image. Proc. Econ. Finance 19, 261–277 (2015).Article 

    Google Scholar 
    27.Sideridis, G. D., Simos, P., Mouzaki, A. & Stamovlasis, D. Efficient word reading: Automaticity of print-related skills indexed by rapid automatized naming through cusp-catastrophe modeling. Sci. Stud. Read. 20, 6–19 (2016).Article 

    Google Scholar 
    28.Diks, C. & Wang, J. Can a stochastic cusp catastrophe model explain housing market crashes?. J. Econ. Dyn. Control 69, 68–88 (2016).Article 

    Google Scholar 
    29.Xu, Y. & Chen, X. Protection motivation theory and cigarette smoking among vocational high school students in China: A cusp catastrophe modeling analysis. Glob. Health Res. Policy 1, 3 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Chen, D.-G., Lin, F., Chen, X., Tang, W. & Kitzman, H. Cusp Catastrophe Model: A nonlinear model for health outcomes in nursing research. Nurs. Res. 63, 211–220 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Mostafa, M. M. Catastrophe theory predicts international concern for global warming. J. Quant. Econ. https://doi.org/10.1007/s40953-020-00199-8 (2020).Article 

    Google Scholar 
    32.Sguotti, C. et al. Non-linearity in stock–recruitment relationships of Atlantic cod: Insights from a multi-model approach. ICES J. Mar. Sci. 77, 1492–1502 (2020).Article 

    Google Scholar 
    33.Forster, P. M., Maycock, A. C., McKenna, C. M. & Smith, C. J. Latest climate models confirm need for urgent mitigation. Nat. Clim. Change 10, 7–10 (2020).ADS 
    Article 

    Google Scholar 
    34.Gröger, M., Arneborg, L., Dieterich, C., Höglund, A. & Meier, H. E. M. Summer hydrographic changes in the Baltic Sea, Kattegat and Skagerrak projected in an ensemble of climate scenarios downscaled with a coupled regional ocean–sea ice–atmosphere model. Clim. Dyn. 53, 5945–5966 (2019).Article 

    Google Scholar 
    35.Litzow, M. A., Mueter, F. J. & Hobday, A. J. Reassessing regime shifts in the North Pacific: Incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability. Glob. Change Biol. 20, 38–50 (2014).ADS 
    Article 

    Google Scholar 
    36.Auber, A., Travers-Trolet, M., Villanueva, M. C. & Ernande, B. Regime shift in an exploited fish community related to natural climate oscillations. PLoS One 10, e0129883 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    37.Karnauskas, M. et al. Evidence of climate-driven ecosystem reorganization in the Gulf of Mexico. Glob. Change Biol. 21, 2554–2568 (2015).ADS 
    Article 

    Google Scholar 
    38.Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Kotta, J. et al. Novel crab predator causes marine ecosystem regime shift. Sci. Rep. 8, 4956 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Vert-pre, K. A., Amoroso, R. O., Jensen, O. P. & Hilborn, R. Frequency and intensity of productivity regime shifts in marine fish stocks. Proc. Natl. Acad. Sci. 110, 1779–1784 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Perretti, C. et al. Regime shifts in fish recruitment on the Northeast US Continental Shelf. Mar. Ecol. Prog. Ser. 574, 1–11 (2017).ADS 
    Article 

    Google Scholar 
    42.Litzow, M. A., Ciannelli, L., Cunningham, C. J., Johnson, B. & Puerta, P. Nonstationary effects of ocean temperature on Pacific salmon productivity. Can. J. Fish. Aquat. Sci. 76, 1923–1928 (2019).Article 

    Google Scholar 
    43.van der Maas, H. L. J., Kolstein, R. & van der Pligt, J. Sudden transitions in attitudes. Sociol. Methods Res. 32, 125–152 (2003).MathSciNet 
    Article 

    Google Scholar 
    44.Griffith, G. P. Closing the gap between causality, prediction, emergence, and applied marine management. ICES J. Mar. Sci. 77, 1456–1462 (2020).Article 

    Google Scholar 
    45.Hutchings, J. A. Collapse and recovery of marine fishes. Nature 406, 882–885 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    46.Hilborn, R., Hively, D. J., Jensen, O. P. & Branch, T. A. The dynamics of fish populations at low abundance and prospects for rebuilding and recovery. ICES J. Mar. Sci. 71, 2141–2151 (2014).Article 

    Google Scholar 
    47.Köster, F. Trophodynamic control by clupeid predators on recruitment success in Baltic cod?. ICES J. Mar. Sci. 57, 310–323 (2000).Article 

    Google Scholar 
    48.Rowe, S., Hutchings, J. A., Bekkevold, D. & Rakitin, A. Depensation, probability of fertilization, and the mating system of Atlantic cod (Gadus morhua L.). ICES J. Mar. Sci. 61, 1144–1150 (2004).Article 

    Google Scholar 
    49.Keith, D. M. & Hutchings, J. A. Population dynamics of marine fishes at low abundance. Can. J. Fish. Aquat. Sci. 69, 1150–1163 (2012).Article 

    Google Scholar 
    50.Kuparinen, A., Keith, D. M. & Hutchings, J. A. Allee effect and the uncertainty of population recovery: Allee effect and population recovery. Conserv. Biol. 28, 790–798 (2014).PubMed 
    Article 

    Google Scholar 
    51.Neuenhoff, R. D. et al. Continued decline of a collapsed population of Atlantic cod (Gadus morhua) due to predation-driven Allee effects. Can. J. Fish. Aquat. Sci. 76, 168–184 (2019).Article 

    Google Scholar 
    52.Vergnon, R., Shin, Y.-J. & Cury, P. Cultivation, Allee effect and resilience of large demersal fish populations. Aquat. Living Resour. 21, 287–295 (2008).Article 

    Google Scholar 
    53.Saha, B., Bhowmick, A. R., Chattopadhyay, J. & Bhattacharya, S. On the evidence of an Allee effect in herring populations and consequences for population survival: A model-based study. Ecol. Model. 250, 72–80 (2013).Article 

    Google Scholar 
    54.Perälä, T. & Kuparinen, A. Detection of Allee effects in marine fishes: Analytical biases generated by data availability and model selection. Proc. R. Soc. B Biol. Sci. 284, 20171284 (2017).Article 

    Google Scholar 
    55.Lundquist, C. J. & Botsford, L. W. Estimating larval production of a broadcast spawner: The influence of density, aggregation, and the fertilization Allee effect. Can. J. Fish. Aquat. Sci. 68, 30–42 (2011).Article 

    Google Scholar 
    56.Sæther, B.-E., Engen, S., Lande, R. & Saether, B.-E. Density-dependence and optimal harvesting of fluctuating populations. Oikos 76, 40 (1996).MATH 
    Article 

    Google Scholar 
    57.Rowe, S. & Hutchings, J. A. Mating systems and the conservation of commercially exploited marine fish. Trends Ecol. Evol. 18, 567–572 (2003).Article 

    Google Scholar 
    58.Swain, D. P. & Chouinard, G. A. Predicted extirpation of the dominant demersal fish in a large marine ecosystem: Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci. 65, 2315–2319 (2008).Article 

    Google Scholar 
    59.Kuparinen, A. & Hutchings, J. A. Increased natural mortality at low abundance can generate an Allee effect in a marine fish. R. Soc. Open Sci. 1, 140075 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Swain, D. & Benoît, H. Extreme increases in natural mortality prevent recovery of collapsed fish populations in a Northwest Atlantic ecosystem. Mar. Ecol. Prog. Ser. 519, 165–182 (2015).ADS 
    Article 

    Google Scholar 
    61.Walters, C. & Kitchell, J. F. Cultivation/depensation effects on juvenile survival and recruitment: Implications for the theory of fishing. Can. J. Fish. Aquat. Sci. 58, 39–50 (2001).Article 

    Google Scholar 
    62.Andreasen, H. et al. Diet composition and food consumption rate of harbor porpoises (Phocoena phocoena) in the western Baltic Sea. Mar. Mamm. Sci. 33, 1053–1079 (2017).Article 

    Google Scholar 
    63.Hüssy, K. Review of western Baltic cod (Gadus morhua) recruitment dynamics. ICES J. Mar. Sci. 68, 1459–1471 (2011).Article 

    Google Scholar 
    64.Winter, A., Richter, A. & Eikeset, A. M. Implications of Allee effects for fisheries management in a changing climate: Evidence from Atlantic cod. Ecol. Appl. 30, 1–14 (2020).65.Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: A global meta-analysis. Fish Fish. 19, 964–973 (2018).Article 

    Google Scholar 
    66.Szuwalski, C. S., Vert-Pre, K. A., Punt, A. E., Branch, T. A. & Hilborn, R. Examining common assumptions about recruitment: A meta-analysis of recruitment dynamics for worldwide marine fisheries. Fish Fish. 16, 633–648 (2015).Article 

    Google Scholar 
    67.Funk, S., Krumme, U., Temming, A. & Möllmann, C. Gillnet fishers’ knowledge reveals seasonality in depth and habitat use of cod (Gadus morhua) in the Western Baltic Sea. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsaa071 (2020).Article 

    Google Scholar 
    68.Hüssy, K., Hinrichsen, H.-H. & Huwer, B. Hydrographic influence on the spawning habitat suitability of western Baltic cod (Gadus morhua). ICES J. Mar. Sci. 69, 1736–1743 (2012).Article 

    Google Scholar 
    69.Hinrichsen, H.-H., Hüssy, K. & Huwer, B. Spatio-temporal variability in western Baltic cod early life stage survival mediated by egg buoyancy, hydrography and hydrodynamics. ICES J. Mar. Sci. 69, 1744–1752 (2012).Article 

    Google Scholar 
    70.Petereit, C., Hinrichsen, H.-H., Franke, A. & Köster, F. Floating along buoyancy levels: Dispersal and survival of western Baltic fish eggs. Prog. Oceanogr. 122, 131–152 (2014).ADS 
    Article 

    Google Scholar 
    71.Stiasny, M. H. et al. Ocean acidification effects on Atlantic Cod larval survival and recruitment to the fished population. PLoS One 11, e0155448 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    72.Voss, R. et al. Ecological-economic sustainability of the Baltic cod fisheries under ocean warming and acidification. J. Environ. Manag. 238, 110–118 (2019).Article 

    Google Scholar 
    73.Lindegren, M., Möllmann, C., Nielsen, A. & Stenseth, N. C. Preventing the collapse of the Baltic cod stock through an ecosystem-based management approach. Proc. Natl. Acad. Sci. 106, 14722–14727 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Lindegren, M. et al. Ecological forecasting under climate change: The case of Baltic cod. Proc. R. Soc. B Biol. Sci. 277, 2121–2130 (2010).Article 

    Google Scholar 
    75.Holsman, K. K. et al. Ecosystem-based fisheries management forestalls climate-driven collapse. Nat. Commun. 11, 4579 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    76.Levin, P. S. et al. Building effective fishery ecosystem plans. Mar. Policy 92, 48–57 (2018).Article 

    Google Scholar 
    77.Dawson, C. & Levin, P. S. Moving the ecosystem-based fisheries management mountain begins by shifting small stones: A critical analysis of EBFM on the U.S. West Coast. Mar. Policy 100, 58–65 (2019).Article 

    Google Scholar 
    78.Link, J. S. & Marshak, A. R. Characterizing and comparing marine fisheries ecosystems in the United States: Determinants of success in moving toward ecosystem-based fisheries management. Rev. Fish Biol. Fish. 29, 23–70 (2019).Article 

    Google Scholar 
    79.Townsend, H. et al. Progress on implementing ecosystem-based fisheries management in the United States through the use of ecosystem models and analysis. Front. Mar. Sci. 6, 641 (2019).Article 

    Google Scholar 
    80.Koehn, L. E. et al. Case studies demonstrate capacity for a structured planning process for ecosystem-based fisheries management. Can. J. Fish. Aquat. Sci. 77, 1256–1274 (2020).Article 

    Google Scholar 
    81.Skern-Mauritzen, M. et al. Ecosystem processes are rarely included in tactical fisheries management. Fish Fish. 17, 165–175 (2016).Article 

    Google Scholar 
    82.Marshall, K. N., Koehn, L. E., Levin, P. S., Essington, T. E. & Jensen, O. P. Inclusion of ecosystem information in US fish stock assessments suggests progress toward ecosystem-based fisheries management. ICES J. Mar. Sci. 76, 1–9 (2019).Article 

    Google Scholar 
    83.Otto, S. A., Kadin, M., Casini, M., Torres, M. A. & Blenckner, T. A quantitative framework for selecting and validating food web indicators. Ecol. Ind. 84, 619–631 (2018).Article 

    Google Scholar 
    84.Kadin, M. et al. Trophic interactions, management trade-offs and climate change: The need for adaptive thresholds to operationalize ecosystem indicators. Front. Mar. Sci. 6, 249 (2019).ADS 
    Article 

    Google Scholar 
    85.Samhouri, J. F. et al. Defining ecosystem thresholds for human activities and environmental pressures in the California Current. Ecosphere 8, 1–21 (2017).86.Payne, M. R. et al. Lessons from the first generation of marine ecological forecast products. Front. Mar. Sci. 4, 289 (2017).Article 

    Google Scholar 
    87.Tommasi, D. et al. Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Prog. Oceanogr. 152, 15–49 (2017).ADS 
    Article 

    Google Scholar 
    88.Haltuch, M. et al. Unraveling the recruitment problem: A review of environmentally-informed forecasting and management strategy evaluation. Fish. Res. 217, 198–216 (2019).Article 

    Google Scholar 
    89.Hobday, A. J. et al. A framework for combining seasonal forecasts and climate projections to aid risk management for fisheries and aquaculture. Front. Mar. Sci. 5, 137 (2018).Article 

    Google Scholar 
    90.Hobday, A. J. et al. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsy210 (2019).Article 

    Google Scholar 
    91.Punt, A. E., Butterworth, D. S., de Moor, C. L., De Oliveira, J. A. A. & Haddon, M. Management strategy evaluation: Best practices. Fish Fish. 17, 303–334 (2016).Article 

    Google Scholar 
    92.Grüss, A. et al. Recommendations on the use of ecosystem modeling for informing ecosystem-based fisheries management and restoration outcomes in the Gulf of Mexico. Mar. Coast. Fish. 9, 281–295 (2017).Article 

    Google Scholar 
    93.Hollowed, A. B. et al. Integrated modeling to evaluate climate change impacts on coupled social-ecological systems in Alaska. Front. Mar. Sci. 6, 775 (2020).Article 

    Google Scholar 
    94.Okamoto, D. K. et al. Attending to spatial social–ecological sensitivities to improve trade-off analysis in natural resource management. Fish Fish. 21, 1–12 (2020).Article 

    Google Scholar 
    95.Möllmann, C. et al. Implementing ecosystem-based fisheries management: From single-species to integrated ecosystem assessment and advice for Baltic Sea fish stocks. ICES J. Mar. Sci. 71, 1187–1197 (2014).Article 

    Google Scholar 
    96.Voss, R. et al. Assessing social—ecological trade-offs to advance ecosystem-based fisheries management. PLoS One 9, e107811 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    97.Schmidt, J. O. et al. Future ocean observations to connect climate, fisheries and marine ecosystems. Front. Mar. Sci. 6, 550 (2019).Article 

    Google Scholar 
    98.Hicks, C. C. et al. Engage key social concepts for sustainability. Science 352, 38–40 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    99.Hornborg, S. et al. Ecosystem-based fisheries management requires broader performance indicators for the human dimension. Mar. Policy 108, 103639 (2019).Article 

    Google Scholar 
    100.Levin, P. S. et al. Conceptualization of social-ecological systems of the california current: An examination of interdisciplinary science supporting ecosystem-based management. Coast. Manag. 44, 397–408 (2016).Article 

    Google Scholar 
    101.ICES. Herring (Clupea harengus) in subdivisions 20-24, spring spawners (Skagerrak, Kattegat, and western Baltic). https://doi.org/10.17895/ICES.ADVICE.4715 (2019).102.Quentin Grafton, R. Adaptation to climate change in marine capture fisheries. Mar. Policy 34, 606–615 (2010).Article 

    Google Scholar 
    103.Lindegren, M. & Brander, K. Adapting fisheries and their management to climate change: A review of concepts, tools, frameworks, and current progress toward implementation. Rev. Fish. Sci. Aquac. 26, 400–415 (2018).Article 

    Google Scholar 
    104.Holsman, K. K. et al. Towards climate resiliency in fisheries management. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsz031 (2019).Article 

    Google Scholar 
    105.Bell, R. J., Odell, J., Kirchner, G. & Lomonico, S. Actions to promote and achieve climate-ready fisheries: Summary of current practice. Mar. Coast. Fish. 12, 166–190 (2020).Article 

    Google Scholar 
    106.Gaichas, S. K., Link, J. S. & Hare, J. A. A risk-based approach to evaluating northeast US fish community vulnerability to climate change. ICES J. Mar. Sci. 71, 2323–2342 (2014).Article 

    Google Scholar 
    107.Pecl, G. T. et al. Rapid assessment of fisheries species sensitivity to climate change. Clim. Change 127, 505–520 (2014).ADS 
    Article 

    Google Scholar 
    108.Hare, J. A. et al. A vulnerability assessment of fish and invertebrates to climate change on the Northeast U.S. Continental Shelf. PLoS One 11, e0146756 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    109.Johnson, J. E. et al. Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support. Mar. Policy 74, 220–229 (2016).Article 

    Google Scholar 
    110.Whitney, C. K. et al. Adaptive capacity: From assessment to action in coastal social-ecological systems. Ecol. Soc. 22, art22 (2017).Article 

    Google Scholar 
    111.Johnson, F. A., Eaton, M. J., Mikels-Carrasco, J. & Case, D. Building adaptive capacity in a coastal region experiencing global change. Ecol. Soc. 25, art9 (2020).Article 

    Google Scholar 
    112.ICES. Baltic Fisheries Assessemant Working Group. (2019). https://doi.org/10.17895/ICES.PUB.5949.113.ICES. Baltic Fisheries Assessemant Working Group. ICES CM 2014/ACOM:10 (2014).114.Hüssy, K. et al. Spatio-temporal trends in stock mixing of eastern and western Baltic cod in the Arkona Basin and the implications for recruitment. ICES J. Mar. Sci. J. Conseil 73, 293–303 (2016).Article 

    Google Scholar 
    115.Weist, P. et al. Assessing SNP-markers to study population mixing and ecological adaptation in Baltic cod. PLoS One 14, e0218127 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    116.R Core Team. R: A Language and Environment for Statistical Computing. (Accessed 2 July 2021); https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).117.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
    Article 

    Google Scholar 
    118.Killick, R. & Eckley, I. A. Changepoint: An R package for changepoint analysis. J. Stat. Softw. 58, 1–19 (2014).119.Zeileis, A., Kleiber, C., Krämer, W. & Hornik, K. Testing and dating of structural changes in practice. Comput. Stat. Data Anal. 44, 109–123 (2003).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    120.Otto, S. A. Comparison of change point detection methods. (Accessed 2 July 2021); https://www.marinedatascience.co/blog/2019/09/28/comparison-of-change-point-detection-methods/. (2019). More

  • in

    Climate change and tree growth in the Khakass-Minusinsk Depression (South Siberia) impacted by large water reservoirs

    1.IPCC. Climate Change 2007: The Physical Science Basis. Contribution of working group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2007).2.IPCC. Special Report on the Impacts of Global Warming of 1.5 °C above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (WMO, 2019).3.Rogers, J. C. & Mosely-Thompson, E. Atlantic Arctic cyclones and mild Siberian winters of the 1980s. Geophys. Res. Lett. 22, 799–802 (1995).ADS 
    Article 

    Google Scholar 
    4.Davi, N. K., Jacoby, G. C., Curtis, A. E. & Baatarbileg, N. Extension of drought records for central Asia using tree rings: West-central Mongolia. J. Clim. 19, 288–299 (2006).ADS 
    Article 

    Google Scholar 
    5.Kattsov, V. M. & Semenov, S. M. Second Roshydromet Assessment Report on Climate Change and its Consequences in Russian Federation (Roshydromet, 2014).
    Google Scholar 
    6.Savelieva, N. I., Semiletov, I. P., Vasilevskaya, L. N. & Pugach, S. P. A climate shift in seasonal values of meteorological and hydrological parameters for Northeastern Asia. Prog. Oceanogr. 47, 279–297 (2000).ADS 
    Article 

    Google Scholar 
    7.Liu, X. et al. Drought evolution and its impact on the crop yield in the North China Plain. J. Hydrol. 564, 984–996 (2018).ADS 
    Article 

    Google Scholar 
    8.Cho, D. J. & Kim, K. Y. Role of Ural blocking in Arctic sea ice loss and its connection with Arctic warming in winter. Clim. Dyn. 56, 1571–1588 (2021).Article 

    Google Scholar 
    9.Savkin, V. M. Reservoirs of Siberia: Consequences of their creation to water ecology and water management facilities. Sib. Ecol. J. 2, 109–121 (2000) (in Russian).
    Google Scholar 
    10.Poff, N. L. & Hart, D. D. How dams vary and why it matters for the emerging science of dam removal: An ecological classification of dams is needed to characterize how the tremendous variation in the size, operational mode, age, and number of dams in a river basin influences the potential for restoring regulated rivers via dam removal. Bioscience 52, 659–668 (2002).Article 

    Google Scholar 
    11.Osika, D. G., Otinova, AYu. & Ponomareva, N. L. About the origin of the global warming and the reasons for the formation of climatic anomalies and disasters. Arid Ecosyst. 19, 104–112 (2013) (in Russian).
    Google Scholar 
    12.Aras, E. Effects of multiple dam projects on river ecology and climate change: Çoruh River Basin, Turkey. Adv. Environ. Res. 7, 121 (2018).
    Google Scholar 
    13.Shen, P. & Zhao, S. 1/4 to 1/3 of observed warming trends in China from 1980 to 2015 are attributed to land use changes. Clim. Change 164, 59. https://doi.org/10.1007/s10584-021-03045-9 (2021).ADS 
    Article 

    Google Scholar 
    14.Ward, J. V. & Stanford, J. A. The Ecology of Regulated Streams (Plenum Press, 1979).Book 

    Google Scholar 
    15.Ligon, F. K., Dietrich, W. E. & Trush, W. J. Downstream ecological effects of dams. Bioscience 45, 183–192 (1995).Article 

    Google Scholar 
    16.Gyau-Boakye, P. Environmental impacts of the Akosombo dam and effects of climate change on the lake levels. Environ. Dev. Sustain. 3, 17–29 (2001).Article 

    Google Scholar 
    17.Muth, R. T. et al. Flow and Temperature Recommendations for Endangered Fishes in the Green River Downstream of Flaming Gorge Dam. Final Report, Upper Colorado River Endangered Fish Recovery Program Project FG-53 (UCREFRP, 2000).18.Degu, A. M. et al. The influence of large dams on surrounding climate and precipitation patterns. Geophys. Res. Lett. 38, L04405. https://doi.org/10.1029/2010GL046482 (2011).ADS 
    Article 

    Google Scholar 
    19.Normatov, I. S., Muminov, A. & Normatov, P. I. The impact of water reservoirs on biodiversity and food security. Creation of adaptation mechanisms. Glob. Perspect. Eng. Manag. 1, 21–25 (2012).
    Google Scholar 
    20.Butorin, N. V., Vendrov, S. L., Dyakonov, K. N., Reteyum, A. Y. & Romanenko, V. I. Effect of the Rybinsk reservoir on the surrounding area. In Man-Made Lakes: Their Problems and Environmental Effects (eds Ackerman, W. C. et al.) 246–250 (American Geophysical Union, 1973).
    Google Scholar 
    21.American Society of Civil Engineers. Guidelines for Retirement of Dams and Hydroelectric Facilities (American Society of Civil Engineers, 1997).
    Google Scholar 
    22.Rosenzweig, C. et al. Assessment of observed changes and responses in natural and managed systems. In Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Parry, M. L. et al.) 79–131 (Cambridge UP, 2007).
    Google Scholar 
    23.Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).ADS 
    Article 

    Google Scholar 
    24.Gill, D. S., Amthor, J. S. & Bormann, F. H. Leaf phenology, photosynthesis, and the persistence of saplings and shrubs in a mature northern hardwood forest. Tree Physiol. 18, 281–289 (1998).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Augspurger, C. K., Cheeseman, J. M. & Salk, C. F. Light gains and physiological capacity of understory woody plants during phenological avoidance of canopy shade. Funct. Ecol. 19, 537–546 (2005).Article 

    Google Scholar 
    26.Zhang, X., Friedl, M. A., Schaaf, C. B. & Strahler, A. H. Climate controls on vegetation phenological patterns in northern mid-and high latitudes inferred from MODIS data. Glob. Chang. Biol. 10, 1133–1145 (2004).ADS 
    Article 

    Google Scholar 
    27.Zeng, H., Jia, G. & Epstein, H. Recent changes in phenology over the northern high latitudes detected from multi-satellite data. Environ. Res. Lett. 6, 045508. https://doi.org/10.1088/1748-9326/6/4/045508 (2011).ADS 
    Article 

    Google Scholar 
    28.Montgomery, R. A., Rice, K. E., Stefanski, A., Rich, R. L. & Reich, P. B. Phenological responses of temperate and boreal trees to warming depend on ambient spring temperatures, leaf habit, and geographic range. PNAS 117, 10397–10405 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Badeck, F.-W. et al. Responses of spring phenology to climate change. New Phytol. 162, 295–309 (2004).Article 

    Google Scholar 
    30.Camarero, J. J., Olano, J. M. & Parras, A. Plastic bimodal xylogenesis in conifers from continental Mediterranean climates. New Phytol. 185, 471–480 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Rossi, S., Girard, M.-J.J. & Morin, H. Lengthening of the duration of xylogenesis engenders disproportionate increases in xylem production. Glob. Chang. Biol. 20, 2261–2271 (2014).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.McCarty, J. P. Ecological consequences of recent climate change. Conserv. Biol. 15, 320–331 (2001).Article 

    Google Scholar 
    33.Aagaard, K. & Carmack, E. C. The role of sea ice and other fresh water in the Arctic circulation. J. Geophys. Res. Oceans 94, 14485–14498 (1989).ADS 
    Article 

    Google Scholar 
    34.Hunt, J. D. et al. Hydropower impact on the river flow of a humid regional climate. Clim. Change 163, 379–393 (2020).ADS 
    Article 

    Google Scholar 
    35.Kosmakov, I. V. Thermal and Ice Regime in the Upper and Lower Reaches of High-Pressure Hydroelectric Power Stations on the Yenisei (Klaretianum, 2001) (in Russian).
    Google Scholar 
    36.Bryzgalov, V. I. From the Experience of Creation and Development of the Krasnoyarsk and Sayano-Shushenskaya Hydroelectric Power Plants (Siberian Publ. House “Surikov,” 1999) (in Russian).
    Google Scholar 
    37.Sheffield, J., Andreadis, K. M. & Wood, E. F. Global and continental drought in the second half of the twentieth century: Severity-area-duration analysis and temporal variability of large-scale events. J. Clim. 22, 1962–1981 (2009).ADS 
    Article 

    Google Scholar 
    38.Liu, H. et al. Rapid warming accelerates tree growth decline in semi-arid forests of Inner Asia. Glob. Change Biol. 19, 2500–2510 (2013).ADS 
    Article 

    Google Scholar 
    39.Stanke, H., Finley, A. O., Domke, G. M., Weed, A. S. & MacFarlane, D. W. Over half of western United States’ most abundant tree species in decline. Nat. Commun. 12, 451. https://doi.org/10.1038/s41467-020-20678-z (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Amrit, K., Pandey, R. P., Mishra, S. K. & Daradur, M. Relationship of drought frequency and severity with range of annual temperature variation. Nat. Hazards 92, 1199–1210 (2018).Article 

    Google Scholar 
    41.Jackson, R. D., Idso, S. B., Reginato, R. J. & Pinter, P. J. Jr. Canopy temperature as a crop water stress indicator. Water Resour. Res. 17(4), 1133–1138 (1981).ADS 
    Article 

    Google Scholar 
    42.Bao, G., Liu, Y. & Linderholm, H. W. April–September mean maximum temperature inferred from Hailar pine (Pinus sylvestris var. mongolica) tree rings in the Hulunbuir region, Inner Mongolia, back to 1868 AD. Palaeogeogr. Palaeoclimatol. Palaeoecol. 313, 162–172 (2012).Article 

    Google Scholar 
    43.de Vrese, P. & Stacke, T. Irrigation and hydrometeorological extremes. Clim. Dyn. 55, 1521–1537 (2020).Article 

    Google Scholar 
    44.Gustokashina, N. N. & Balybina, A. S. Variation in the natural-climatic characteristics of the territory adjacent to the reservoirs of the Angara chain of power plants. Geogr. Nat. Res. 4, 93–100 (2005) (in Russian).
    Google Scholar 
    45.Arzac, A. et al. Increasing radial and latewood growth rates of Larix cajanderi Mayr. and Pinus sylvestris L. in the continuous permafrost zone in Central Yakutia (Russia). Ann. For. Sci. 76, 96 (2019).Article 

    Google Scholar 
    46.Gower, S. T. & Richards, J. H. Larches: Deciduous conifers in an evergreen world. Bioscience 40, 818–826 (1990).Article 

    Google Scholar 
    47.McDowell, N. et al. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought?. New Phytol. 178, 719–739 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Piper, F. I. & Fajardo, A. Foliar habit, tolerance to defoliation and their link to carbon and nitrogen storage. J. Ecol. 102, 1101–1111 (2014).CAS 
    Article 

    Google Scholar 
    49.Khansaritoreh, E., Schuldt, B. & Dulamsuren, C. Hydraulic traits and tree-ring width in Larix sibirica Ledeb. as affected by summer drought and forest fragmentation in the Mongolian forest steppe. Ann. For. Sci. 75, 30. https://doi.org/10.1007/s13595-018-0701-2 (2018).Article 

    Google Scholar 
    50.Urban, J., Rubtsov, A. V., Urban, A. V., Shashkin, A. V. & Benkova, V. E. Canopy transpiration of a Larix sibirica and Pinus sylvestris forest in Central Siberia. Agric. For. Meteorol. 271, 64–72 (2019).ADS 
    Article 

    Google Scholar 
    51.Kolari, P., Lappalainen, H. K., HäNninen, H. & Hari, P. Relationship between temperature and the seasonal course of photosynthesis in Scots pine at northern timberline and in southern boreal zone. Tellus B Chem. Phys. Meteorol. 59, 542–552 (2007).ADS 
    Article 
    CAS 

    Google Scholar 
    52.Wu, J., Guan, D., Yuan, F., Wang, A. & Jin, C. Soil temperature triggers the onset of photosynthesis in Korean pine. PLoS ONE 8, e65401. https://doi.org/10.1371/journal.pone.0065401 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Yang, Q. et al. Two dominant boreal conifers use contrasting mechanisms to reactivate photosynthesis in the spring. Nat. Commun. 11, 128. https://doi.org/10.1038/s41467-019-13954-0 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Tanja, S. et al. Air temperature triggers the recovery of evergreen boreal forest photosynthesis in spring. Glob. Change Biol. 9, 1410–1426 (2003).ADS 
    Article 

    Google Scholar 
    55.Sevanto, S. et al. Wintertime photosynthesis and water uptake in a boreal forest. Tree Physiol. 26, 749–757 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Rossi, S. et al. Critical temperatures for xylogenesis in conifers of cold climates. Glob. Ecol. Biogeogr. 17, 696–707 (2008).Article 

    Google Scholar 
    57.Babushkina, E. A., Belokopytova, L. V., Zhirnova, D. F. & Vaganov, E. A. Siberian spruce tree ring anatomy: Imprint of development processes and their high-temporal environmental regulation. Dendrochronologia 53, 114–124 (2019).Article 

    Google Scholar 
    58.Cannell, M. G. R. & Smith, R. I. Climatic warming, spring budburst and forest damage on trees. J. Appl. Ecol. 23, 177–191 (1986).Article 

    Google Scholar 
    59.Bertin, R. I. Plant phenology and distribution in relation to recent climate change. J. Torrey Bot. Soc. 135, 126–146 (2008).Article 

    Google Scholar 
    60.Ziaco, E., Biondi, F., Rossi, S. & Deslauriers, A. Environmental drivers of cambial phenology in Great Basin bristlecone pine. Tree Physiol. 36, 818–831 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Rahman, M. H. et al. Winter-spring temperature pattern is closely related to the onset of cambial reactivation in stems of the evergreen conifer Chamaecyparis pisifera. Sci. Rep. 10, 14341. https://doi.org/10.1038/s41598-020-70356-9 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Katz, R. W. & Brown, B. G. Extreme events in a changing climate: Variability is more important than averages. Clim. Chang. 21, 289–302 (1992).ADS 
    Article 

    Google Scholar 
    63.Germain, S. J. & Lutz, J. A. Climate extremes may be more important than climate means when predicting species range shifts. Clim. Chang. 163, 579–598 (2020).ADS 
    Article 

    Google Scholar 
    64.Vendrov, S. L., Avakyan, A. B., Dyakonov, K. N. & Reteyum, A. Y. The Role of Reservoirs in Changing Natural Conditions (Znaniye, 1968) (in Russian).
    Google Scholar 
    65.Stivari, S. M., De Oliveira, A. P. & Soares, J. On the climate impact of the local circulation in the Itaipu Lake area. Clim. Chang. 72, 103–121 (2005).ADS 
    Article 

    Google Scholar 
    66.Wilks, D. S. Statistical Methods in the Atmospheric Sciences 4th edn. (Elsevier, 2019).
    Google Scholar 
    67.Arguez, A. & Vose, R. S. The definition of the standard WMO climate normal: The key to deriving alternative climate normals. Bull. Am. Meteorol. Soc. 92, 699–704 (2011).ADS 
    Article 

    Google Scholar 
    68.Rosgidromet. Guidelines for the Compilation of Agrometeorological Yearbook for the Agricultural Zone of the Russian Federation. Guiding Document 52.33.725–2010 (Russian Scientific Research Institute of Hydrometeorological Information, World Data Center, 2010) (in Russian).69.Chae, H. et al. Local variability in temperature, humidity and radiation in the Baekdu Daegan Mountain protected area of Korea. J. Mt. Sci. 9, 613–627 (2012).Article 

    Google Scholar 
    70.Wypych, A., Ustrnul, Z. & Schmatz, D. R. Long-term variability of air temperature and precipitation conditions in the Polish Carpathians. J. Mt. Sci. 15, 237–253 (2018).Article 

    Google Scholar 
    71.Selyaninov, G. T. About climate agricultural estimation. Proc. Agric. Meteorol. 20, 165–177 (1928) (in Russian).
    Google Scholar 
    72.Babushkina, E. A., Belokopytova, L. V., Grachev, A. M., Meko, D. M. & Vaganov, E. A. Variation of the hydrological regime of Bele-Shira closed basin in Southern Siberia and its reflection in the radial growth of Larix sibirica. Reg. Environ. Change. 17, 1725–1737 (2017).Article 

    Google Scholar 
    73.Cook, E. R. & Kairiukstis, L. A. Methods of Dendrochronology. Application in Environmental Sciences (Kluwer Academic Publishers, 1990).Book 

    Google Scholar 
    74.Rinn, F. TSAP-Win: Time Series Analysis and Presentation for Dendrochronology and Related Applications: User Reference (RINNTECH, 2003).
    Google Scholar 
    75.Holmes, R. L. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 43, 69–78 (1983).
    Google Scholar 
    76.Grissino-Mayer, H. D. Evaluating crossdating accuracy: A manual and tutorial for the computer program COFECHA. Tree-Ring Res. 57, 205–221 (2001).
    Google Scholar 
    77.Cook, E. R, Krusic, P. J., Holmes, R. H. & Peters, K. Program ARSTAN Ver. ARS41d. https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software (2007).78.Strackee, J. & Jansma, E. The statistical properties of mean sensitivity—A reappraisal. Dendrochronologia 10, 121–135 (1992).
    Google Scholar 
    79.Wigley, T. M. L., Briffa, K. R. & Jones, P. D. On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Appl. Meteorol. Climatol. 23, 201–213 (1984).ADS 
    Article 

    Google Scholar 
    80.Yasmeen, S. et al. Contrasting climate-growth relationship between Larix gmelinii and Pinus sylvestris var. mongolica along a latitudinal gradient in Daxing’an Mountains, China. Dendrochronologia 58, 125645. https://doi.org/10.1016/j.dendro.2019.125645 (2019).Article 

    Google Scholar  More

  • in

    Biosynthetic potential of uncultured Antarctic soil bacteria revealed through long-read metagenomic sequencing

    Soil diversity, taxonomic classification and binning of BGCsNonpareil analysis estimated an abundance-weighted coverage of 85.3% for the 44.4 Gb used in the long-read assembly. To achieve 95% and 99% coverage, respectively, 250 Gb and 1.6 Tb of sequencing were predicted to be necessary. Alpha diversity was estimated at Nd = 21.6. Contigs were binned using CONCOCT, MaxBin2 and MetaBAT2, consensus bins were generated using metaWRAP refine and classified using GTDB-Tk. This yielded 114 bacterial bins with CheckM completeness  > 50% and contamination  More

  • in

    The limits of SARS-CoV-2 predictability

    If an endpoint of continued circulation (endemicity rather than eradication) seems likely, this still leaves us with questions about the range of outbreak sizes, their intensity and seasonality. Surprisingly, some basic epidemiological parameters for predicting these dynamical features are still uncertain. For example, R0, the reproductive number, which captures the infectiousness of the pathogen, is typically measured from the growth of the epidemic and is harder to estimate once non-pharmaceutical interventions (NPIs) are in place. Similarly, changes to R0 for evolved SARS- CoV-2 variants are difficult to ascertain given simultaneous changes to behaviour and interventions. It is not yet clear whether there is an evolutionary limit to strain infectiousness. To date, structural changes to the SARS-CoV-2 spike furin cleavage site7 as well as enhanced binding of the receptor binding domain to the human ACE2 receptor8 have been associated with enhanced transmissibility in variant strains, but in the longer term, transmission increases may saturate and viral evolution may modulate other aspects of disease transmission including host susceptibility. Nevertheless, any present or future changes to R0 will affect long-term epidemic dynamics, including the intensity of outbreaks and the age-structure of infections.The transmission of many respiratory pathogens varies seasonally, driven either by climatic factors or seasonal changes in behaviour such as schooling. The role of climate in driving transmission of SARS-CoV-2 is currently unclear: high susceptibility during the early pandemic likely limited any climate effect9, and statistical analyses of the climate-SARS-CoV-2 link have been confounded by trends in the data and regional differences in reporting and control measures. This has not been helped by the relatively short case time series (that is, just over a year’s worth of data) compared to typical climate–disease studies that look for climate links over many seasons. An alternative line of evidence comes from the four endemic coronaviruses, which exhibit seasonal wintertime outbreaks. It is possible that SARS-CoV-2 will follow suit. Disentangling the climate drivers of SARS-CoV-2 will become easier over time as both longer time series are available, and susceptibility declines9.A further question is the extent to which SARS-CoV-2 endemic dynamics will be affected by interactions with other circulating pathogens, including the endemic coronaviruses. Both modelling and laboratory work implies a degree of cross-immunity between coronaviruses10,11,12. The NPIs put in place to limit the spread of SARS-CoV-2 have also limited the circulation of many other pathogens, such that infection interactions have not been observed in current case trajectories13. However, as NPIs are relaxed, signatures of cross-species interactions will likely become increasingly visible.Beyond cross-immunity with other pathogens, the longitudinal trajectory of immunity, as depicted in Fig. 1, will play a crucial role in determining SARS-CoV-2 endemic dynamics14. For immunizing infections, susceptibility is driven by birth rates, and infections may be concentrated in younger age groups. For infections with waning immunity or antigenic evolution, susceptibility is driven by the rate at which immunity wanes or the rate the pathogen evolves as well as characteristics of secondary infections. The disease dynamics of pathogens with high rates of antigenic evolution are particularly hard to predict: evolved strains may have variable transmission rates and manifest variable immune responses. An analogy can be made with influenza, where the size and intensity of the seasonal influenza peak is typically very difficult to forecast15.The future course of SARS-CoV-2 remains uncertain. The next few months to a year represents a critical time where we will begin to develop an understanding of key parameters, such as the strength and duration of vaccinal and natural immunity, the seasonality of transmission and the possible interaction of SARS-CoV-2 with other circulating pathogens. In combination, these parameters will allow improved prediction of both long-term SARS-CoV-2 epidemic dynamics, as well as the likelihood of elimination and eradication. An area of particular focus will be the rate of antigenic evolution and the extent to which vaccines remain protective against evolved strains. In all scenarios, rapid and equitable distribution of vaccines presents the greatest hope for minimizing future severe outbreaks. More