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    Influence of the intertropical convergence zone on early cretaceous plant distribution in the South Atlantic

    The pre-evaporitic, evaporitic, and post-evaporitic phases are recognized for the late Aptian. These phases are recorded within the K40–K50 sequences (Fig. 2A), and show an average maximum thickness of approximately 650 m in the studied basins. The pre-evaporitic phase is represented by carbonate and siliciclastic deposits formed in fluvial and lacustrine deltaic environments within a large proto-oceanic gulf28 (Fig. 2A). The peak of the evaporitic deposition is recorded in the K50 sequence, with widespread occurrences in the Brazilian equatorial margin. The origin of these deposits is the heat intensification associated with the widening of the Atlantic Ocean. These conditions caused strong evaporation leading to a wide distribution of evaporites (mainly halite and anhydrite gypsum) in the South Atlantic basins. The eastern continental margin of Brazil contains a restricted marine section characterized by evaporites, which are particularly prominent in thickness and occurrence in the Espírito Santo Basin (Itaúnas Member of the Mariricu Formation) and the Sergipe Basin (the Ibura Member of the Muribeca Formation)28. Evaporites form the most prominent evidence of dry climates in the South Atlantic basins11, with evaporation exceeding precipitation. The post-evaporitic phase is characterized by fully marine conditions evidenced by rich assemblages of marine fossils. During this phase, carbonates were deposited, followed by muddy and sandy sediments in shallow-marine and slope environments.Figure 2Paleoclimatic phases scheme and principal component analysis for paleoclimatic phases. (A) Paleoclimatic phases scheme for the late Aptian and the main depositional environments. (B) Principal component plot of bioclimatic groups. (C) Principal component for the pre-evaporitic phase (N = 92), evaporitic phase (N = 78), and post-evaporitic phase (N = 385); see Supplementary Fig. 9 for individual basins.Full size imagePaleovegetationWe identified a rich plant community with 139 spore and pollen genera/morphotypes representing all plant groups: bryophytes (five genera), ferns (58 genera), lycophytes (18 genera), pteridosperms (one genus), gymnosperms (27 genera), and angiosperms (30 genera) (Supplementary Table 2). The inferred systematic affinities at the family level reached 100% in bryophytes, 56.9% in ferns, 100% in lycophytes, 100% in pteridosperms, 92.6% in gymnosperms, and 40.0% in angiosperms, totaling 67.6% of the recorded genera (Supplementary Table 2). Marine elements (e.g., dinoflagellate cysts and microforaminiferal linings) were identified, in particular from the Sergipe and Araripe basins (Fig. 1). Pollen grains from gymnosperms were most abundant, represented mainly by the conifer families Cheirolepidiaceae, Araucariaceae, and Podocarpaceae, although representing different climatic settings. Classopollis (Cheirolepidiaceae) is the most abundant genus in all sections studied, followed by Araucariacites (Araucariaceae). Gymnosperms showed low diversity. Spore-producing plants are the most diverse in the assemblages of all basins (82 genera) and represented by several families of bryophytes, ferns, and lycophytes (e.g., Sphagnaceae, Anemiaceae, Cyatheaceae, Marsileaceae, Selaginellaceae, and Lycopodiaceae). These plant groups depend on water to reproduce and are therefore associated with humid settings.Cicatricosisporites (Anemiaceae) is the third most abundant palynomorph in all the basins, but especially in the northeastern basins (e.g., Sergipe Basin). Angiosperms are among the least abundant; however, they are diverse and include the most abundant and controversial genus Afropollis, herein attributed to angiosperms. In the most recent publication that addressed this question, ref.29 suggest that Afropollis should be treated as an angiosperm genus, although without more precise systematic assignment. The 30 genera/morphotypes of angiosperms are assigned to 8 families, viz., Arecaceae, Chloranthaceae, Euphorbiaceae, Flacourtiaceae, Illiciaceae, Liliaceae, Solanaceae and Trimeniaceae. The second most abundant genus is Stellatopollis also without precise systematic assignment.Spatio-temporal distribution of bioclimatic groupsOn the basis of their botanical affinities, most taxa were classified into five bioclimatic groups [see “Methods” section and Supplementary information], viz., hydrophytes, hygrophytes, tropical lowland flora, upland flora, and xerophytes (Supplementary Table 2) (Fig. 3).Figure 3Relevant palynomorphs of bioclimatic groups: (1) Aequitriradites sp.; (2) Crybelosporites sp.; (3) Perotriletes sp.; (4) Cicatricosisporites sp.; (5) Echinatisporis sp.; (6) Verrucosisporites sp.; (7) Bennettitaepollenites sp.; (8) Stellatopollis sp.; (9) Afropollis sp.; (10) Dejaxpollenites microfoveolatus; (11) Classopollis classoides; (12) Equisetosporites ovatus; (13) Gnetaceaepollenites jansonii; (14) Regalipollenites sp.; (15) Araucariacites sp.; (16) Callialasporites dampieri; (17) Complicatissacus cearensis; (18) Cyathidites sp.. Scale bar 20 µm.Full size imageOverall, the vegetation is dominated by the xerophytic bioclimatic group on account of the very high abundance of Classopollis (Cheirolepidiaceae) (general mean of 60.5%). However, the stratigraphic distribution of the bioclimatic groups in the sections studied (Supplementary Figs. 1–6) indicates wet phases confirmed by the curves of the other bioclimatic groups (hygrophytes, hydrophytes, tropical lowland flora, and upland flora). We used Pearson correlation analysis (Supplementary Fig. 7) to assess the correlation between the bioclimatic groups. The analysis revealed positive correlations between the bioclimatic groups of hygrophytes, hydrophytes, tropical lowland flora, and upland flora, and a negative correlation between these groups and the xerophyte group (Supplementary Fig. 7). The positive correlation between upland flora and hygrophytes confirms previous studies for the Sergipe Basin6,7, suggesting a relation between these groups and the hot and humid climate. The weak negative correlation between tropical lowland flora and upland flora is presumably related to elevation.The upland flora forms the second most abundant bioclimatic group, with an average of 18.9%. The large number of specimens of Araucariacites (Araucariaceae) in this group is notable. The hydrophytes are the least abundant group, with an average of only 1.4%. In this group, the highest values are attributed to the genus Crybelosporites (Marsileaceae).Principal component analyses (PCA) were used to reduce the multidimensional dataset, based on the percent abundance of the bioclimatic groups to a smaller number of dimensions for interpretive analysis. For all sections, two components or axes explain 97.6% of the observed variability (Fig. 2B). Hygrophytes, hydrophytes, tropical lowland flora, and upland flora show positive correlation (positive loading, 0.320, 0.029, 0.006, and 0.468, respectively), whereas xerophytes show a negative relationship (negative loading, − 0.823) on the first axis, which alone explains 83.0% of the variability. In summary, the first axis of the PCA reveals a separation of two major climatic conditions (wet and dry) along the axis (Fig. 2B). The wet conditions include the associations of hygrophytes, hydrophytes, tropical lowland flora, and upland flora, with dry conditions associated with taxa from the xerophyte group. The second axis explains 14.6%, in which hygrophytes, hydrophytes, and tropical lowland flora show a positive correlation relationship (positive loading, 0.719, 0.037, 0.036, respectively), whereas upland flora and xerophytes show a negative relationship (negative loading, − 0.684 and − 0.108, respectively). With respect to the second axis, a polarization between the hygrophytes (positive loading, 0.719) and the upland flora (negative loading, − 0.684) can be interpreted as a lowland–upland trend. The same pattern was recorded for all paleoclimatic phases (Fig. 2C) and sections (Supplementary Fig. 8), that is, the first axis is related to humidity vs. aridity, and the second axis to elevation (lowland vs. upland). This suggests that these two factors, particularly the first one, controlled the vegetation distribution in the late Aptian of the region. As all bioclimatic groups occurred in the three evaporitic phases, these trends in abundance reflect expansion and contraction of the recorded vegetation.Parallel increasing trends of bioclimatic groups mark the pre-evaporitic phase: hygrophytes and upland flora in the Bragança-Viseu, São Luís, Parnaíba, Ceará, Potiguar, and Araripe basins (Supplementary Figs. 1–3 and 5), suggesting that there was a certain amount of moisture in these areas. The xerophytes show the lowest average of this phase (44.1%) (Table 1), whereas hygrophytes show the highest average (27.0%). These humid conditions are confirmed by the highest mean of the Fs/X ratio (Fs/X = 0.4), representing the predominance of spore-producing plants [see Methods section and Supplementary information]. Despite the low abundance of hydrophytes in the sections, a prominent feature is the highest average (2.5%) of this group (Table 1), which is assigned to aquatic environments, confirming relatively wet conditions in this phase. There are no pre-evaporitic samples available from the Sergipe and Espírito Santo basins.Table 1 Average abundance of bioclimatic groups, diversity, Fs/X and marine elements for the paleoclimatic phases.Full size tableThe evaporitic phase is characterized by the highest abundance of the xerophyte bioclimatic group (76.4%) (Table 1), represented mainly by Classopollis (Supplementary Figs. 1–6). A high abundance of xerophytes occurred widely distributed in all basins studied. In this phase, tropical lowland flora is notable, showing an average higher than the overall average (3.3%), particularly in the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins (Supplementary Figs. 1 and 2). This result is related to the moderate to high abundance of the genus Afropollis in these basins. The evaporitic phase is also characterized by the lowest average Fs/X ratio (Fs/X = 0.1) (Table 1), confirming the dominance of xerophytes.The post-evaporitic phase is characterized by the upland flora bioclimatic group (mean = 24.4%) (Table 1). The moderate to high abundance of upland flora in this phase is represented, in particular, by pollen grains of Araucariacites, which represent the high-relief family Araucariaceae. This bioclimatic group is associated with more humid conditions, as confirmed by an Fs/X ratio higher than the overall average (Fs/X = 0.2). The upland flora is significant in all basins, except the Espírito Santo Basin, where xerophytes predominate in both studied phases in this basin.Latitudinal biome distributionsBiome change is a fundamental biological response to climate change. In the study area, the predominance of a specific biome is mainly related to humidity, since all five recorded bioclimatic groups are related to a warm climate (Supplementary Table 2) representing two biomes: tropical xerophytic shrubland and tropical rainforest. In the rainforest biome two phytophysiognomies are recognized: lowland and montane rainforest. The tropical xerophytic shrubland biome predominates in the three paleoclimatic phases, with a wide latitudinal range from the Bragança-Viseu, São Luís, and Parnaíba basins (1° S) to the Espírito Santo Basin (20° S). This wide distribution is compatible with a predominantly arid climate in South America in the late Aptian, as indicated by paleoclimatic maps8,9,15 (Fig. 4A). Most arid and semi-arid ecosystems are mainly controlled by precipitation. Other climate parameters are less important, a condition that simplifies cause-effect interpretations. The PCA (Fig. 2B) demonstrated that the wet–dry trend, which reflects high–low precipitation, was the main determinant in the distribution of the biomes. However, considering all phases, an increasing trend in humidity was observed from the southeast (Espírito Santo Basin) to the northeast (e.g., Potiguar Basin) (Fig. 4B), coinciding with the hot and wet belt attributed to the ITCZ (Fig. 4A)15. The latitudinal distribution of diversity also follows this trend. Diversity increased significantly towards in the basins near the equator. Diversity indices (Shannon – H’) peaked in the Sergipe Basin (H’ = 3.5, CL-47 section) at 11° S. Conversely, the lowest average diversity is recorded in the Espírito Santo Basin (H’ = 1.1) at 20° S. Additionally, there is a clear correlation between high diversity (H’) and humidity (Fs/X ratio) (r = 0.691), regardless of paleoclimatic phase, as evidenced by the synchronicity of the H’ and Fs/X curves (Fig. 5). After data normalization between humidity (Fs/X) and marine elements (dinoflagellate cysts and microforaminifer linings), we performed linear correlation analyses, which showed a weak but positive correlation (r = 0.137). This is due to the fact that pre- evaporitic deposits contain only 19 occurrences of dinoflagellate cysts in 90 samples. Despite this, the curves of Fs/X, marine elements and diversity are synchronous (Fig. 5), suggesting a relation between humidity, diversity, and marine incursions.Figure 4Latitudinal changes in late Aptian biomes from southeast to center-north. (A) Paleoclimatic belts of the late Aptian in South America (climatic belts modified from refer.14). Reconstruction map at 116 Ma modified from ODSN Plate Tectonic Reconstruction Service. The Reconstruction map at 116 Ma was generated by ODSN Plate Tectonic Reconstruction Service (https://www.odsn.de/odsn/services/paleomap/paleomap.html). (B) Late Aptian latitudinal distribution of the tropical xerophytic biome in Brazil. (C) Stratigraphic distribution of biomes for individual basins. (D) Relative Importance of biomes for paleoclimatic phases.Full size imageFigure 5Biome trends in relation to paleoclimatic phases. Change in biomes, diversity, Fs/X ratio and marine elements shown by changepoint analysis plotted against paleoclimatic phases.Full size imageThe pre-evaporitic phase is marked by a certain balance between the biomes (Fig. 4C,D). In the lowlands, the tropical xerophytic shrubland biome predominated in the Bragança- Viseu, São Luís, Parnaíba, and Ceará basins, but in the Potiguar Basin it is co-dominant with the lowland rainforest. The montane rainforest was relatively extensive in this phase, although with several areal changes, and reached its widest extent in the Araripe (7° S) and Potiguar (5° S) basins in response to the deterioration of the tropical xerophytic shrubland biome. These conditions demonstrate that humidity was relatively high at this stage. The pre-evaporitic deposits were characterized by the highest diversity average (H’ = 1.8).The method of indicator species analysis (IndVal) was used to identify the key species of each paleoclimatic phase (Supplementary Table 15). The species identified for the pre-evaporitic phase, Deltoidospora spp. (Cyatheaceae-Dicksoniaceae) related to the montane rainforest, are indicator species for the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins. The Gnetaceaepollenites spp. (Gnetaceae) of the Potiguar Basin and Equisetosporites spp. (Ephedraceae) of the Araripe Basin are related to the tropical xerophytic shrubland biome (Supplementary Table 15). Even for the pre-evaporitic phase, a progressive increase in the tropical xerophytic shrubland biome was observed and interpreted as the start of a climatic deterioration stage (Fig. 4C), which culminated in the evaporitic phase. Shifts in vegetation types may occur when precipitation reaches a threshold value, which means that a regionally synchronous gradual climate change can cause abrupt vegetation shifts. The change from humid to warm and arid conditions (evaporitic phase) is directly related to a decrease in precipitation. This aridization process coincides with the appearance of marine elements (e.g., dinoflagellate cysts). The threshold effect (intense evaporation) is reflected in an abrupt decrease in the abundance of lowland and montane rainforest and a sharp increase to a very high abundance of the tropical xerophytic shrubland biome (Supplementary Figs. 4C and 5). The threshold effect was not detected in the Espírito Santo Basin, where the arid conditions remained stable with minimal shift (expansion and contraction) of the biome. The main representatives of this biome are conifers of the family Cheirolepidiaceae (Classopollis), which were most abundant in lagoons and coastal environments and are often associated with evaporates30,31,32,33,34,35. Even under xeric or water-stressed conditions there was a slight increase in biomes related to a humid climate (lowland and montane rainforest phytophysiognomies) towards the equatorial region, suggesting influence of the ITCZ (Fig. 4A,B).The evaporitic phase was characterized by the lowest diversity average (H’ = 1.2). With modest rainfall, arid regions are generally characterized by fewer species than moister biomes36. However, diversity indices peaked in the Bragança-Viseu, São Luís, and Parnaíba basins (H’ = 2.6, RL-01 section) and along the equatorial margin (2° S) (Supplementary Fig. 1).IndVal emphasizes the xeric conditions in the evaporitic phase by association with the species from the tropical xerophytic shrubland biome: Classopollis spp. (Ceará and Potiguar basins), Classopollis classoides (Sergipe Basin), Classopollis intrareticulatus (Araripe Basin), and Gnetaceaepollenites spp. (Espírito Santo Basin). For the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins, where xeric restrictions are milder, the indicator taxon is Afropollis spp. from the lowland rainforest. This genus shows the weakest negative correlation with xerophytes.After the end of evaporite deposition, all sections indicate climatic stability, which kept the climate hot and arid even in the post-evaporitic phase, although the response was not linear.The shift in the biomes, especially the tropical xerophytic shrubland in the Bragança-Viseu, São Luís, Parnaíba, Ceará, and Araripe basins, occurred in the transition between the evaporitic and post-evaporitic phases, whereas in the Potiguar and Sergipe basins it occurred within the post-evaporitic phase. As indicated in the dendrograms of each section (Supplementary Figs. 1–6), the shift occurred abruptly in all basins, except the Espírito Santo Basin. The tropical rainforest biome (lowland and montane rainforests) replaced the tropical xerophytic shrubland in almost all basins (Fig. 4C). Even the Espírito Santo Basin, far from the influence of the ITCZ, shows a slight increase in lowland rainforest. The changes in the biomes are attributable to threshold effects caused by gradual climate change related to the ITCZ intensification shift and progressive increase in marine influence, indicated by an increase in marine microplankton from an average of 3.9% in the evaporitic phase to 44.1%. The increase in marine influence is reflected in the first major flooding surface observed in the Cretaceous succession27. Thus, a climate amelioration stage was established in the post- evaporitic phase (Fig. 5). In combination with published paleotopographic information25, the bioclimatic groups associated to the humid conditions (hygrophytes, hydrophytes, tropical lowland flora, and upland flora) were combined and visualized to create Fig. 6.Figure 6Reconstruction of the transitional gradient between marine to terrestrial environment (uplands) under ITCZ influence. The illustration is based on paleoflora and environmental information from palynological data from studied sections. Original size illustration: 18 × 24 cm, by Julio Lacerda.Full size imageAccording to refs.7,37, arid conditions are characterized by sea-level lowstands, whereas warm and humid conditions are correlated with sea levels rise, which explains the increase in the tropical rainforest biome (lowland and montane rainforests). The more intense humidity is supported by the results of IndVal for the post-evaporitic phase, with all species related to humid climate: Deltoidospora spp. (Bragança-Viseu, São Luís and Parnaíba basins), Araucariacites limbatus (Ceará Basin), Cicatricosisporites spp. (Potiguar Basin), Cicatricosisporites spp. and Araucariacites australis (Sergipe Basin), Inaperturopollenites spp. (Araripe Basin) and Inaperturopollenites simplex (Espírito Santo Basin).Our results show that the ITCZ combined with the opening of the South Atlantic Ocean during the late Aptian altered vegetation dynamics. As today, the ITCZ influence is stronger in the northeastern and north-central regions of South America. It is notable that the late Aptian climate evolution in the South Atlantic, culminating in higher humidity, was accompanied by an intrinsic relation between plant diversity, humidity, and marine influence. More

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    Sexual selection for males with beneficial mutations

    Charlesworth, D., Barton, N. H. & Charlesworth, B. The sources of adaptive variation. Proc. R. Soc. B Biol. Sci. 284(1855), 20162864 (1855).Article 
    CAS 

    Google Scholar 
    Whitlock, M. C. Fixation of new alleles and the extinction of small populations: Drift load, beneficial alleles, and sexual selection. Evolution 54(6), 1855–1861 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hamilton, W. D. & Zuk, M. Heritable true fitness and bright birds: a role for parasites?. Science 218(4570), 384 (1982).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Hadany, L. & Beker, T. Sexual selection and the evolution of obligatory sex. BMC Evol. Biol. 7(1), 245 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Clutton-Brock, T. Reproductive competition and sexual selection. Philos. Trans. R. Soc. B Biol. Sci. 372(1729), 20160310 (2017).Article 

    Google Scholar 
    Taddei, F. et al. Role of mutator alleles in adaptive evolution. Nature 387(6634), 700–702 (1997).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Agrawal, A. F. & Wang, A. D. Increased transmission of mutations by low-condition females: Evidence for condition-dependent DNA repair. PLoS Biol. 6(2), e30 (2008).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Petrie, M. & Roberts, G. Sexual selection and the evolution of evolvability. Heredity 98(4), 198–205 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dugand, R. J., Kennington, W. J. & Tomkins, J. L. Evolutionary divergence in competitive mating success through female mating bias for good genes. Sci. Adv. 4(5), eaaq0369 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Siller, S. Sexual selection and the maintenance of sex. Nature 411(6838), 689–692 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Agrawal, A. F. Sexual selection and the maintenance of sexual reproduction. Nature 411(6838), 692–695 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Lehtonen, J., Jennions, M. D. & Kokko, H. The many costs of sex. Trends Ecol. Evol. 27(3), 172–178 (2012).PubMed 
    Article 

    Google Scholar 
    Maynard Smith, J. What use is sex?. J. Theor. Biol. 30(2), 319–335 (1971).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    Trivers, R. L. Parental investment and sexual selection. In Sexual Selection and the Descent of Man 1871–1971 (ed. Campbell, B.) 136–179 (Aldone, 1972).
    Google Scholar 
    Petrie, M. & Lipsitch, M. Avian polygyny is most likely in populations with high variability in heritable male fitness. Proc. R. Soc. Lond. Ser. B Biol. Sci. 256(1347), 275–280 (1994).ADS 
    Article 

    Google Scholar 
    Lumley, A. J. et al. Sexual selection protects against extinction. Nature 522(7557), 470–473 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Andersson, M. Sexual Selection (Princeton University Press, 1994).Book 

    Google Scholar 
    Petrie, M. Improved growth and survival of offspring of peacocks with more elaborate trains. Nature 371(6498), 598–599 (1994).ADS 
    CAS 
    Article 

    Google Scholar 
    Møller, A. P. & Alatalo, R. V. Good-genes effects in sexual selection. Proc. R. Soc. Lond. Ser. B Biol. Sci. 266(1414), 85–91 (1999).Article 

    Google Scholar 
    David, P. et al. Condition-dependent signalling of genetic variation in stalk-eyed flies. Nature 406(6792), 186–188 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Hale, M. L. et al. Is the peacock’s train an honest signal of genetic quality at the major histocompatibility complex?. J. Evol. Biol. 22(6), 1284–1294 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Prokop, Z. M. et al. Meta-analysis suggests choosy females get sexy sons more than “good genes”. Evolution 66(9), 2665–2673 (2012).PubMed 
    Article 

    Google Scholar 
    Kokko, H. et al. The sexual selection continuum. Proc. R. Soc. Lond. Ser. B Biol. Sci. 269(1498), 1331–1340 (2002).Article 

    Google Scholar 
    Drake, J. W. et al. Rates of Spontaneous Mutation. Genetics 148(4), 1667 (1998).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Keightley, P. D. Rates and fitness consequences of new mutations in humans. Genetics 190(2), 295–304 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Haag-Liautard, C. et al. Direct estimation of per nucleotide and genomic deleterious mutation rates in Drosophila. Nature 445(7123), 82–85 (2007).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Metzgar, D. & Wills, C. Evidence for the adaptive evolution of mutation rates. Cell 101(6), 581–584 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Janetos, A. C. Strategies of female mate choice: A theoretical analysis. Behav. Ecol. Sociobiol. 7(2), 107–112 (1980).Article 

    Google Scholar 
    Johnstone, R. A. & Earn, D. J. D. Imperfect female choice and male mating skew on leks of different sizes. Behav. Ecol. Sociobiol. 45(3), 277–281 (1999).Article 

    Google Scholar 
    Petrie, M., Halliday, T. & Sanders, C. Peahens prefer peacocks with elaborate trains. Anim. Behav. 41(2), 323–331 (1991).Article 

    Google Scholar 
    Cally, J. G., Stuart-Fox, D. & Holman, L. Meta-analytic evidence that sexual selection improves population fitness. Nat. Commun. 10(1), 2017 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kotiaho, J. S. et al. On the resolution of the lek paradox. Trends Ecol. Evol. 23(1), 1–3 (2008).PubMed 
    Article 

    Google Scholar 
    Parker, G. A., Baker, R. R. & Smith, V. G. F. The origin and evolution of gamete dimorphism and the male-female phenomenon. J. Theor. Biol. 36(3), 529–553 (1972).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Parker, G. A. The sexual cascade and the rise of pre-ejaculatory (Darwinian) sexual selection, sex roles, and sexual conflict. Cold Spring Harb. Perspect. Biol. 6(10), a017509–a017509 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rowe, L. & Houle, D. The lek paradox and the capture of genetic variance by condition dependent traits. Proc. R. Soc. Lond. Ser. B Biol. Sci. 263(1375), 1415–1421 (1996).ADS 
    Article 

    Google Scholar 
    Petrie, M. Evolution by sexual selection. Front. Ecol. Evol. 9, 950 (2021).Article 

    Google Scholar 
    Petrie, M. & Kempenaers, B. Extra-pair paternity in birds: Explaining variation between species and populations. Trends Ecol. Evol. 13(2), 52–58 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Møller, A. P. & Cuervo, J. J. Minisatellite mutation rates increase with extra-pair paternity among birds. BMC Evol. Biol. 9(1), 100 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Anmarkrud, J. A. et al. Factors affecting germline mutations in a hypervariable microsatellite: A comparative analysis of six species of swallows (Aves: Hirundinidae). Mutat. Res. Fundam. Mol. Mech. Mutagen. 708(1), 37–43 (2011).CAS 
    Article 

    Google Scholar 
    Ellegren, H. Characteristics, causes and evolutionary consequences of male-biased mutation. Proc. R. Soc. B Biol. Sci. 274(1606), 1–10 (2007).CAS 
    Article 

    Google Scholar 
    Baur, J. & Berger, D. Experimental evidence for effects of sexual selection on condition-dependent mutation rates. Nat. Ecol. Evol. 4, 737–744 (2020).PubMed 
    Article 

    Google Scholar 
    Vrijenhoek, R. C. & Parker, E. D. Geographical parthenogenesis: General purpose genotypes and frozen niche variation. In Lost Sex (eds Schön, I. et al.) (Springer, Dordrecht, 2009).
    Google Scholar 
    Reudink, M. W. et al. Evolution of song and color in island birds. Wilson J. Ornithol. 133(1), 1–10 (2021).Article 

    Google Scholar 
    Iglesias-Carrasco, M. et al. Sexual selection, body mass and molecular evolution interact to predict diversification in birds. Proc. R. Soc. B Biol. Sci. 2019(286), 20190172 (1899).
    Google Scholar 
    Earl, D. J. & Deem, M. W. Evolvability is a selectable trait. Proc. Natl. Acad. Sci. U. S. A. 101(32), 11531 (2004).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

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    Thermodynamic basis for the demarcation of Arctic and alpine treelines

    Explaining the heterogeneous organization of vegetation across landscapes has proved both a puzzling and an inspiring concept as patterns have formed naturally across the world. One such pattern is the existence of treelines, i.e., the demarcation zone between forestland and vegetation without trees1,2. There is a large body of work with developed and competing theories for understanding the specific limits and drivers for the non-existence of trees beyond a treeline. Yet after decades of study, there is still debate among ecologists and biologists over the mechanisms that limit the presence of trees beyond treelines. Current explanations are rooted in, but not limited to, consideration of factors such as excessive light and wind, limited CO(_2), and low temperatures1,3,4,5,6,7.
    With this in mind, we ask: Is there another perspective that could provide insights complementary to and beyond what has been developed through the prevailing mechanistic approach? While the existing explanations are based on ideas of structural stability (e.g., high winds above the treeline) and limited resources pertaining to water, energy, and nutrients1,3,4,5,6,7, we instead examine the question of what determines the existence of a treeline from the perspective of thermodynamic feasibility. Our premise is that the existence or non-existence of certain vegetation first and foremost has to be ascertained through thermodynamic feasibility or infeasibility, respectively. Therefore, we approach the question of the existence of treelines by asking: If certain vegetation does not exist at a given location, is there a role that the thermodynamic perspective can play in telling us that its existence is infeasible? By approaching the topic from the thermodynamic perspective, we seek to provide important complementary insight to the broad base of scientific understanding ecologists and biologists have developed to explain the existence of treelines. Further, this work lays out additional context for the discussion around the advance of treelines (e.g., why some treelines advance and others do not).For example, several theories assert that the stature of vegetation is limited by CO(_2) balance and photosynthetic requirements under harsh winter conditions1,6. Other hypotheses argue that plant life is instead limited by the atmospheric temperature and the local environments that the plants experience7,8. Is there a perspective that could unify both of these findings? Through this work, we demonstrate how thermodynamic infeasibility inferred from model simulations pertaining to counterfactual scenarios manifests through both of these physiological limits. This means that either of these limits, individually or together, could lead to the nonexistence of trees—which limiting factor is expressed first varies by location. Thus, the commonality among locations that have different limiting mechanisms can be found in the unifying concept of thermodynamic infeasibility. While CO(_2) limitation may prevail in one location and temperature-related constraints may be limiting in another, both lead to thermodynamic infeasibility, meaning that the thermal environment results in a mechanistic infeasibility, such as net CO(_2) loss. In the examples presented in this paper, thermodynamic infeasibility manifests through negative work associated with constraints arising from temperature gradients and net CO(_2) loss, demonstrating that both limitations can be encapsulated using the thermodynamic perspective.Ecosystem thermodynamicsIt is now generally accepted that observed patterns of vegetation composition and its organization are a result of self-organization, or the spontaneous emergence of pattern without external predetermination9,10. By framing ecosystems as open thermodynamic systems, we explore further the concept of thermodynamic feasibility and its role in the self-organization of vegetation structure. Vegetation structure consists of composition (i.e., the number and type of functional groups11) and organizational patterns on the landscape12. We focus on composition rather than the spatial pattern of vegetation organization. We utilize a one-dimensional ecohydrological model that incorporates representative functional groups with no lateral transport of energy or matter under the assumption that the vegetation composition and pattern remain spatially uniform at a given site. Thus, we are able to compare the vertical thermodynamic regimes of proximal ecosystems with varying vegetation composition. We present the case that observed organization reflected in the demarcation of differing vegetation structures on either side of a treeline is established in tandem with vertical thermodynamic gradients at a given location, driven by the incoming solar energy into an ecosystem. In other words, we hypothesize that beyond a treeline, the existence of trees is prevented by conditions of thermodynamic infeasibility.The application of thermodynamic theory to ecology has been studied for the better part of the last century through the introduction of theoretical thermodynamic properties, such as entropy and exergy, into environmental systems. This work asserts that open thermodynamic systems will evolve based on the strength of applied concentration gradients on the system and will undergo irreversible processes to dissipate energy and destroy these gradients through all means available13,14. In the context of ecosystems, fluxes of mass or energy from the external environment (i.e., above the canopy) result in concentration gradients within the system itself. State variables will transition along these gradients according to the second law of thermodynamics. When the magnitude of incoming energy and consequent spatial imbalance of energy becomes great enough, dissipative structures spontaneously emerge, or self-organize, and establish temperature gradients consistent with the dissipative need of the ecosystem13,15. In this paper we conceptualize the work performed by an ecosystem as its ability to dissipate these applied concentration gradients. Consequently, work is highly dependent upon the existence and composition of self-organized vegetation.In classical thermodynamics, work is performed due to a transfer, or physical movement, of heat15. In the context of ecosystems, work performed by an ecosystem is represented by the exchange of heat with the external environment outside the ecosystem control volume12. Work performed by an ecosystem is, therefore, estimated as the vertical transport of heat in the form of latent and sensible heat, driven by the vertical gradient in temperature within the control volume structured by both the incoming downward shortwave and longwave radiation and the vegetation structure. The bottom boundary of the ecosystem control volumes studied are significantly deep such that heat exchanges due to water infiltration at this interface are insignificant in magnitude relative to latent and sensible heat flux out of the top of the control volume above the canopy. Further, we ignore the substantially slower thermodynamic processes associated with geochemistry in the soil.The vertical temperature gradient creates a directionality of dissipation of incident radiation as heat leaves out of the ecosystem from higher surface temperatures to lower air temperatures. Throughout this paper, we measure work through the net sum of heat leaving the ecosystem as latent and sensible heat—which can either be positive or negative depending on the direction of the resultant temperature gradient (see “Thermodynamic Calculations” in the “Methods” section). This temperature gradient (Eq. 1) emerges as a result of self-organization through feedback between the incoming shortwave and longwave radiation, local environmental conditions, and the heat dissipation and work performed by the vegetation. The presence of ground cover, such as snow, is impacted by aboveground vegetation structure, which provides a physical buffer between the atmosphere and the ground, further influencing the thermal environment and temperature gradient.Although significant research has been conducted by studying plant response to snowpack7,16,17, including the physiological requirements for life under prolonged snowpack and alpine climatic conditions, the thermodynamic perspective provides further insight. In addition to the physiological/mechanistic response of plants to snowpack and other environmental conditions, the thermal regime of a column of land experiencing snowpack is fundamentally different when an ecosystem does or does not have plants with stature taller than the height of snowpack (e.g., trees). Presence of trees results in shading from solar radiation and a physical buffer between the earth/snow surface and the atmosphere. Thus, the thermal profile of an ecosystem reveals valuable information about ecosystem behavior, and there is a need to explore the thermodynamic relationship between solar radiation and vegetation composition under varying environmental conditions. Thus, through this paper we define the circumstances under which multiple functional groups that include trees are no longer feasible for the available solar radiation leading to demarcated zones identifiable as treelines.Work by Körner argues that the “climate [that] plants experience” is different than the ambient temperature7. By modeling the layers within the canopy of plants with differing stand heights and leaf distributions, we are able to characterize the thermal regime and the “climate [that] plants experience” throughout the course of a given year. This characterization helps us understand the fundamental changes in behavior under varying environmental conditions with and without trees.An ecosystem’s ability to perform work manifests into four distinct cases depending on the sign of the resultant temperature gradient and the net loss or gain of heat driven by the thermal environment derived from present ground cover, such as vegetation or snow: (1) First and most common during the day when photosynthesis is occurring, the temperature of the earth surface, which receives the solar radiation, is typically warmer than the air above the canopy, and heat leaves the ecosystem upward along the negative temperature gradient, corresponding to a positive work (Fig. 1a). (2) Even when the temperature of the earth surface is warmer than the air above the canopy, there can be situations when there is a net heat gain within the ecosystem, meaning that heat moves into the ecosystem against the direction of the temperature gradient. This case is rare and counterproductive to heat dissipation, corresponding to negative work. (3) Common during the night, temperature inversion emerges. In this case, the temperature gradient from the earth surface to the atmosphere can become positive, meaning that the temperature of the air above the canopy is greater than the temperature of the earth surface. As heat enters the ecosystem to warm the surface, positive work is performed since the heat is still moving along a negative temperature gradient into the ecosystem (Fig. 1b). (4) During snowmelt conditions during the day, particularly for Arctic and alpine ecosystems, temperature inversions also emerge18,19. When this occurs and the ecosystem experiences a net heat loss through latent and sensible heat from the canopy, the heat leaving the ecosystem travels opposite of the direction dictated by the temperature gradient. Thus, in this case, ecosystems perform negative work. Our findings demonstrate how extended periods of time in this last case of work lead to thermodynamic infeasibility for the alpine/Arctic ecosystem counterfactual vegetation scenarios; i.e., ecosystems with vegetation properties from below the treelines cannot be sustained under the environmental conditions above the treelines, and, hence, they do not occur in nature.A recent study concluded that at sites where multiple functional groups exist (e.g., forests), the vegetation structure in which all groups co-exist and interact is thermodynamically more advantageous and, thus, more likely to occur than any one of the individual functional groups that the forest comprises12. Thermodynamic advantage is defined by the production of larger fluxes of entropy, more work performed, and higher work efficiency – a quantity that captures how much of the incoming energy is converted into forms useful for actively dissipating heat. It is possible to envision that under certain environmental conditions, the thermodynamic advantage offered by the existence of multiple functional groups is not tenable, indicating a thermodynamic infeasibility. Thermodynamic infeasibility occurs when a particular vegetation structure is not supported by the thermal environment at a given location. The demarcation exhibited by treelines presents an ideal case to explore this scenario, in that there is a distinct transition from multiple functional groups below the treeline to a single functional group above.Research questionIn this paper, we examine vegetation above and below Arctic and alpine treelines to determine whether the absence of trees in ecosystems above treelines are a result of thermodynamic infeasibility. Simply speaking, we seek to answer the following research question: Is the non-existence of trees beyond the transition zone demarcated as a treeline a reflection of thermodynamic infeasibility associated with the presence of trees, and if so, how is this infeasibility exhibited?Figure 1Conceptual diagram of temperature gradients. The W+ arrow indicates the positive direction of work performed through heat transport. Although in different directions, in both cases (a) and (b), the work performed is positive because heat moves from high to low temperatures. (a) Typical summertime temperature gradients from the earth surface to the air above the canopy are negative for the two real scenarios: subalpine/sub-Arctic forest (left) and alpine tundra/Arctic meadow (right). (b) A conceptual temperature inversion, or positive temperature gradient, which arise when alpine/Arctic forest are simulated as counterfactuals.Full size imageTo address this question, we use an extensively validated multi-layer 1-D physics-based ecohydrological model, MLCan12,20,21,22,23,24,25,26, consisting of 20 above-ground layers, 1 ground surface layer, and 12 below-ground layers (see Supplementary Material). This model is chosen because of its ability to capture interactions among functional groups, such as the impact of shading on understory vegetation and the resulting thermal environment within the canopy23. To balance model performance and accuracy, standing plant species are aggregated into functional groups (i.e., evergreen needleleaf trees, shrubs, grasses; see Table 1) based on literature27,28,29,30. The model output is used to compare the thermodynamic work performed at paired sites above and below the respective treelines for three different locations: the Italian Alps (IT), the United States Rocky Mountains (US), and the Western Canadian Taiga-Tundra (CA) (Fig. 2; see Site Descriptions). For each site pair, four scenarios are performed (Table 1): (1) The subalpine/sub-Arctic forest ecosystems are modeled as they exist with multiple functional groups (Fig. 1a, left). (2) The alpine/Arctic ecosystems are modeled as they exist with one functional group (i.e., shrubs or grasses; Fig. 1a, right). (3) We construct counterfactual scenarios above the treeline in which the vegetation of the subalpine or sub-Arctic forest is simulated with the environmental conditions and parameters of the alpine meadow or Arctic tundra (i.e., adding hypothetical trees where none exist; Fig. 1b). (4) As a control, a final counterfactual scenario is constructed below the treeline in which we model the understory of the subalpine/sub-Arctic forest individually (i.e., removing trees from the existing ecosystem).Table 1 Simulation scenarios with observed and hypothetical vegetation.Full size tableThe simulation of these four scenarios facilitates comparison of the existing vegetation structure of each site with the corresponding counterfactual scenarios. By varying the model inputs of vegetation present at each site while holding the environmental conditions and site-specific parameters consistent, we are able to directly compare thermodynamic outcomes as a result of varying vegetation structure and determine whether the counterfactual scenario with the simulated forest is thermodynamically feasible. Model performance was judged based on comparison to observed heat fluxes, such as latent and sensible heat (see Supplementary Material, Figs. S1–S3). As detailed below, the analysis supports the conclusion that thermodynamic feasibility is an important and complementary condition to the usual considerations of resource availability, such as water and nutrients, which determines the organizing form and function of ecosystems. More

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    Coronamoeba villafranca gen. nov. sp. nov. (Amoebozoa, Dermamoebida) challenges the correlation of morphology and phylogeny in Amoebozoa

    Adl, S. M. et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J. Eukaryot. Microbiol. 66, 4–119. https://doi.org/10.1111/jeu.12691 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Smirnov, A. Amoebas, Lobose. In Encyclopedia of Microbiology (ed. Schaechter, M.) 191–212 (Elsevier, 2012).
    Google Scholar 
    Schaeffer, A. A. Taxonomy of the Amoebas: With Descriptions of Thirty-Nine New Marine and Freshwater Species (Carnegie Inst, 1926).
    Google Scholar 
    Page, F. C. The classification of “naked” amoebae (Phylum Rhizopoda). Arch. Protistenkd. 133, 199–217. https://doi.org/10.1016/S0003-9365(87)80053-2 (1987).Article 

    Google Scholar 
    Page, F. C. A New Key to Freshwater and Soil Gymnamoebae (Freshwater Biological Association, 1988).
    Google Scholar 
    Smirnov, A. V. & Goodkov, A. V. An illustrated list of basic morphotypes of Gymnamoebia (Rhizopoda, Lobosea). Protistology 1, 20–29 (1999).
    Google Scholar 
    Smirnov, A. V. & Brown, S. Guide to the methods of study and identification of soil gymnamoebae. Protistology 3, 148–190 (2004).
    Google Scholar 
    Bovee, E. C. & Jahn, T. L. Mechanisms of movement in taxonomy of Sarcodina. II. The organization of subclasses and orders in relationship to the classes Autotractea and Hydraulea. Am. Midland Nat. 73, 293–298. https://doi.org/10.2307/2423456 (1965).Article 

    Google Scholar 
    Bovee, E. C. & Jahn, T. L. Mechanisms of movement in taxonomy or sarcodina. III. Orders, suborders, families, and subfamilies in the superorder Lobida. Syst. Zool. 15, 229–240. https://doi.org/10.2307/sysbio/15.3.229 (1966).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bovee, E.C. & Sawyer, T.K. Marine Flora and Fauna of the Northeastern United States. Protozoa: Sarcodina: Amoebae. (NOAA Technical Report, 1979). https://doi.org/10.5962/bhl.title.63225.Jahn, T. L. & Bovee, E. C. Mechanisms of movement in taxonomy of Sarcodina. I. As a basis for a new major dichotomy into two classes, Autotractea and Hydraulea. Am. Midl. Nat. 73, 30–40. https://doi.org/10.2307/2423319 (1965).Article 

    Google Scholar 
    Jahn, T. L., Bovee, E. C. & Griffith, D. L. Taxonomy and evolution of the Sarcodina: A reclassification. Taxon 23, 483–496. https://doi.org/10.2307/1218771 (1974).Article 

    Google Scholar 
    Cavalier-Smith, T., Chao, E.E.-Y. & Oates, B. Molecular phylogeny of Amoebozoa and the evolutionary significance of the unikont Phalansterium. Eur. J. Protistol. 40, 21–48. https://doi.org/10.1016/j.ejop.2003.10.001 (2004).Article 

    Google Scholar 
    Smirnov, A. et al. Molecular phylogeny and classification of the lobose amoebae. Protist 156, 129–142. https://doi.org/10.1016/j.protis.2005.06.002 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    Amaral Zettler, L. A. et al. A molecular reassessment of the leptomyxid amoebae. Protist 151, 275–282. https://doi.org/10.1078/1434-4610-00025 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bolivar, I., Fahrni, J. F., Smirnov, A. & Pawlowski, J. SSU rRNA-based phylogenetic position of the genera Amoeba and Chaos (Lobosea, Gymnamoebia): The origin of gymnamoebae revisited. Mol. Biol. Evol. 18, 2306–2314. https://doi.org/10.1093/oxfordjournals.molbev.a003777 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    Fahrni, J. F. et al. Phylogeny of lobose amoebae based on actin and small-subunit ribosomal RNA genes. Mol. Biol. Evol. 20, 1881–1886. https://doi.org/10.1093/molbev/msg201 (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    Cavalier-Smith, T. et al. Multigene phylogeny resolves deep branching of Amoebozoa. Mol. Phylogenet. Evol. 83, 293–304. https://doi.org/10.1016/j.ympev.2014.08.011 (2015).Article 
    PubMed 

    Google Scholar 
    Cavalier-Smith, T., Chao, E. E. & Lewis, R. 187-gene phylogeny of protozoan phylum Amoebozoa reveals a new class (Cutosea) of deep-branching, ultrastructurally unique, enveloped marine Lobosa and clarifies amoeba evolution. Mol. Phylogenet. Evol. 99, 275–296. https://doi.org/10.1016/j.ympev.2016.03.023 (2016).Article 
    PubMed 

    Google Scholar 
    Kang, S. et al. Between a pod and a hard test: The deep evolution of amoebae. Mol. Biol. Evol. 34, 2258–2270. https://doi.org/10.1093/molbev/msx162 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tekle, Y. I. & Wood, F. C. Longamoebia is not monophyletic: Phylogenomic and cytoskeleton analyses provide novel and well-resolved relationships of amoebozoan subclades. Mol. Phylogenet. Evol. 114, 249–260. https://doi.org/10.1016/j.ympev.2017.06.019 (2017).Article 
    PubMed 

    Google Scholar 
    Tekle, Y. I., Wang, F., Wood, F. C., Anderson, O. R. & Smirnov, A. New insights on the evolutionary relationships between the major lineages of Amoebozoa. bioRxiv https://doi.org/10.1101/2022.02.28.482369 (2022).Article 

    Google Scholar 
    Van Wichelen, J. et al. A hotspot of amoebae diversity: 8 new naked amoebae associated with the planktonic bloom-forming cyanobacterium microcystis. Acta Protozool. 55, 61–87. https://doi.org/10.4467/16890027AP.16.007.4942 (2016).Article 

    Google Scholar 
    Janicki, C. Paramoebenstudien (P. pigmentifera Grassi und P. chaetognathi Grassi). Z. Wiss. Zool. 103, 449–518 (1912).
    Google Scholar 
    Volkova, E. & Kudryavtsev, A. A morphological and molecular reinvestigation of Janickina pigmentifera (Grassi, 1881) Chatton 1953—an amoebozoan parasite of arrow-worms (Chaetognatha). Int. J. Syst. Evol. Microbiol. 71, 005094. https://doi.org/10.1099/ijsem.0.005094 (2021).CAS 
    Article 

    Google Scholar 
    Page, F. C. Taxonomic criteria for limax amoebae, with descriptions of 3 new species of Hartmannella and 3 of Vahlkampfia. J. Protozool. 14, 499–521 (1967).CAS 
    Article 

    Google Scholar 
    Page, F. C. & Blanton, R. L. The Heterolobosea (Sarcodina: Rhizopoda), a new class uniting the Schizopyrenida and the Acrasidae (Acrasida). Protistologica 21, 121–132 (1985).
    Google Scholar 
    Laurin, V., Labbé, N., Parent, S., Juteau, P. & Villemur, R. Microeukaryote diversity in a marine methanol-fed fluidized denitrification system. Microb. Ecol. 56, 637–648. https://doi.org/10.1007/s00248-008-9383-x (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Page, F. C. A further study of taxonomic criteria for limax amoebae, with descriptions of new species and a key to genera. Arch. Protistenkd. 116, 149–184 (1974).
    Google Scholar 
    Page, F. C. Marine Gymnamoebae (Institute of Terrestrial Ecology, 1983).
    Google Scholar 
    Page, F. C. A light- and electron-microscopical comparison of limax and flabellate marine amoebae belonging to four genera. Protistologica 16, 57–78 (1980).
    Google Scholar 
    Kuiper, M. W. et al. Quantitative detection of the free-living amoeba Hartmannella vermiformis in surface water by using real-time PCR. Appl. Environ. Microbiol. 72, 5750–5756. https://doi.org/10.1128/AEM.00085-06 (2006).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Smirnov, A., Chao, E., Nassonova, E. & Cavalier-Smith, T. A revised classification of naked lobose amoebae (Amoebozoa: Lobosa). Protist 162, 545–570. https://doi.org/10.1016/j.protis.2011.04.004 (2011).Article 
    PubMed 

    Google Scholar 
    Page, F. C. & Blakey, S. M. Cell surface structure as a taxonomic character in the Thecamoebidae (Protozoa: Gymnamoebia). Zool. J. Linn. Soc. 66, 113–135. https://doi.org/10.1111/j.1096-3642.1979.tb01905.x (1979).Article 

    Google Scholar 
    Smirnov, A. V. & Goodkov, A. V. Paradermamoeba valamo gen. n., sp. n. (Gymnamoebia, Thecamoebidae)—a freshwater amoeba from bottom sediments. Zool. Zhurn. 72, 5–11 (1993) (In Russian with English summary).
    Google Scholar 
    Smirnov, A. & Goodkov, A. Ultrastructure and geographic distribution of the genus Paradermamoeba (Gymnamoebia, Thecamoebidae). Eur. J. Protistol. 40, 113–118. https://doi.org/10.1016/j.ejop.2003.12.001 (2004).Article 

    Google Scholar 
    Smirnov, A. V., Bedjagina, O. M. & Goodkov, A. V. Dermamoeba algensis n sp (Amoebozoa, Dermamoebidae)—an algivorous lobose amoeba with complex cell coat and unusual feeding mode. Eur. J. Protistol. 47, 67–78. https://doi.org/10.1016/j.ejop.2010.12.002 (2011).Article 
    PubMed 

    Google Scholar 
    Bailey, G. B., Day, D. B. & McCoomer, N. E. Entamoeba motility: Dynamics of cytoplasmic streaming, locomotion and translocation of surface-bound particles, and organization of the actin cytoskeleton in Entamoeba invadens. J. Protozool. 39, 267–272. https://doi.org/10.1111/j.1550-7408.1992.tb01313.x (1992).CAS 
    Article 
    PubMed 

    Google Scholar 
    Shiratori, T. & Ishida, K. I. Entamoeba marina n. sp.; a new species of Entamoeba isolated from tidal flat sediment of Iriomote Island, Okinawa, Japan. J. Eukaryot. Microbiol. 63, 280–286. https://doi.org/10.1111/jeu.12276 (2016).Article 
    PubMed 

    Google Scholar 
    Lahr, D. J., Laughinghouse, H. D. IV., Oliverio, A. M., Gao, F. & Katz, L. A. How discordant morphological and molecular evolution among microorganisms can revise our notions of biodiversity on Earth. BioEssays 36, 950–959. https://doi.org/10.1002/bies.201400056 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pomorski, P. et al. Actin dynamics in Amoeba proteus motility. Protoplasma 231, 31–41. https://doi.org/10.1007/s00709-007-0243-1 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    Rogerson, A., Anderson, O. R. & Vogel, C. Are planktonic naked amoebae predominately floc associated or free in the water column?. J. Plankton Res. 25, 1359–1365. https://doi.org/10.1093/plankt/fbg102 (2003).Article 

    Google Scholar 
    Kudryavtsev, A. Paravannella minima n. g. n. sp. (Discosea, Vannellidae) and distinction of the genera in the vannellid amoebae. Eur. J. Protistol. 50, 258–269. https://doi.org/10.1016/j.ejop.2013.12.004 (2014).Article 
    PubMed 

    Google Scholar 
    Kudryavtsev, A., Völcker, E., Clauß, S. & Pawlowski, J. Ovalopodium rosalinum sp. nov., Planopodium haveli gen. nov, sp. nov., Planopodium desertum comb. nov. and new insights into phylogeny of the deeply branching members of the order Himatismenida (Amoebozoa). Int. J. Sys. Evol. Microbiol. 71, 004737. https://doi.org/10.1099/ijsem.0.004737 (2021).CAS 
    Article 

    Google Scholar 
    Blandenier, Q. et al. Mycamoeba gemmipara nov. gen., nov. sp., the first cultured member of the environmental Dermamoebidae clade LKM74 and its unusual life cycle. J. Eukaryot. Microbiol. 64, 257–265. https://doi.org/10.1111/jeu.12357 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kudryavtsev, A. & Volkova, E. Cunea russae n. sp. (Amoebozoa, Dactylopodida), another cryptic species of Cunea Kudryavtsev and Pawlowski, 2015, inhabits a continental brackish-water biotope. Eur. J. Protistol. 73, 125685. https://doi.org/10.1016/j.ejop.2020.125685 (2020).Article 
    PubMed 

    Google Scholar 
    Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682. https://doi.org/10.1038/nmeth.2019 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Maniatis, T., Fritsch, E. F. & Sambrook, J. Molecular Cloning, A Laboratory Manual (Cold Spring Harbor Laboratory, 1982).
    Google Scholar 
    Kudryavtsev, A. & Pawlowski, J. Cunea n. g. (Amoebozoa, Dactylopodida) with two cryptic species isolated from different areas of the ocean. Eur. J. Protistol. 51, 197–209. https://doi.org/10.1016/j.ejop.2015.04.002 (2015).Article 
    PubMed 

    Google Scholar 
    Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA coding regions. Gene 71, 491–499. https://doi.org/10.1016/0378-1119(88)90066-2 (1988).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yoon, H. S. et al. Broadly sampled multigene trees of eukaryotes. BMC Evol. Biol. 8, 14. https://doi.org/10.1186/1471-2148-8-14 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2 (1990).CAS 
    Article 
    PubMed 

    Google Scholar 
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973. https://doi.org/10.1093/bioinformatics/btp348 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gouy, M., Tannier, E., Comte, N. & Parsons, D. P. Seaview version 5: A multiplatform software for multiple sequence alignment, molecular phylogenetic analyses, and tree reconciliation. In Multiple Sequence Alignment. Methods in Molecular Biology (ed. Katoh, K.) 241–260 (Humana, 2021). https://doi.org/10.1007/978-1-0716-1036-7_15.Chapter 

    Google Scholar 
    Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ronquist, F. et al. MRBAYES 3.2: Efficient Bayesian phylogenetic inference and model selection across a large model space. Syst. Biol. 61, 539–542. https://doi.org/10.1093/sysbio/sys029 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Le, S. Q. & Gascuel, O. An improved general amino acid replacement matrix. Mol. Biol. Evol. 25, 1307–1320. https://doi.org/10.1093/molbev/msn067 (2008).CAS 
    Article 
    PubMed 

    Google Scholar  More

  • in

    Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations

    Dean R, Van Kan JA, Pretorius ZA, Hammond-Kosack KE, Di Pietro A, Spanu PD, et al. The Top 10 fungal pathogens in molecular plant pathology. Mol Plant Pathol. 2012;13:414–30.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mansfield J, Genin S, Magori S, Citovsky V, Sriariyanum M, Ronald P, et al. Top 10 plant pathogenic bacteria in molecular plant pathology. Mol Plant Pathol. 2012;13:614–29.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Campbell CL, Noe JP. The spatial analysis of soilborne pathogens and root diseases. Annu Rev Phytopathol. 1985;23:129–48.Article 

    Google Scholar 
    Genin S, Denny TP. Pathogenomics of the Ralstonia solanacearum species complex. Annu Rev Phytopathol. 2012;50:67–89.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kwak MJ, Kong HG, Choi K, Kwon SK, Song JY, Lee J, et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat Biotechnol. 2018;36:1100–9.CAS 
    Article 

    Google Scholar 
    Wei Z, Gu Y, Friman V-P, Kowalchuk GA, Xu Y, Shen Q, et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci Adv. 2019;5:eaaw0759.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lee SM, Kong HG, Song GC, Ryu CM. Disruption of Firmicutes and Actinobacteria abundance in tomato rhizosphere causes the incidence of bacterial wilt disease. ISME J 2021;15:330–47.CAS 
    PubMed 
    Article 

    Google Scholar 
    Berendsen RL, Pieterse CM, Bakker PA. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17:478–86.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hu J, Wei Z, Kowalchuk GA, Xu Y, Shen Q, Jousset A. Rhizosphere microbiome functional diversity and pathogen invasion resistance build up during plant development. Environ Microbiol. 2020;22:5005–18.PubMed 
    Article 

    Google Scholar 
    Faust K, Lahti L, Gonze D, de Vos WM, Raes J. Metagenomics meets time series analysis: unraveling microbial community dynamics. Curr Opin Microbiol. 2015;25:56–66.PubMed 
    Article 

    Google Scholar 
    Fuentes-Chust C, Parolo C, Rosati G, Rivas L, Perez-Toralla K, Simon S, et al. The microbiome meets nanotechnology: opportunities and challenges in developing new diagnostic devices. Adv Mater. 2021;33:e2006104.PubMed 
    Article 
    CAS 

    Google Scholar 
    Schlaberg R. Microbiome diagnostics. Clin Chem. 2020;66:68–76.PubMed 
    Article 

    Google Scholar 
    Xiao Y, Yang C, Yu L, Tian F, Wu Y, Zhao J, et al. Human gut-derived B. longum subsp. longum strains protect against aging in a D-galactose-induced aging mouse model. Microbiome. 2021;9:180.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Petrova MI, Lievens E, Malik S, Imholz N, Lebeer S. Lactobacillus species as biomarkers and agents that can promote various aspects of vaginal health. Front Physiol. 2015;6:81.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wei Z, Hu J, Gu Y, Yin S, Xu Y, Jousset A, et al. Ralstonia solanacearum pathogen disrupts bacterial rhizosphere microbiome during an invasion. Soil Biol Biochem. 2018;118:8–17.CAS 
    Article 

    Google Scholar 
    Gu S, Wei Z, Shao Z, Friman V-P, Cao K, Yang T, et al. Competition for iron drives phytopathogen control by natural rhizosphere microbiomes. Nat Microbiol. 2020;5:1002–10.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wei Z, Yang T, Friman V-P, Xu Y, Shen Q, Jousset A. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat Commun. 2015;6:8413.CAS 
    PubMed 
    Article 

    Google Scholar 
    Li M, Pommier T, Yin Y, Wang J, Gu S, Jousset A, et al. Indirect reduction of Ralstonia solanacearum via pathogen helper inhibition. ISME J 2022;16:868–75.CAS 
    PubMed 
    Article 

    Google Scholar 
    Dubinkina V, Fridman Y, Pandey PP, Maslov S. Multistability and regime shifts in microbial communities explained by competition for essential nutrients. Elife 2019;8:e49720.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: networks, competition, and stability. Science 2015;350:663–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Garcia-Palacios P, Vandegehuchte ML, Shaw EA, Dam M, Post KH, Ramirez KS, et al. Are there links between responses of soil microbes and ecosystem functioning to elevated CO2, N deposition and warming? A global perspective. Glob Chang Biol 2015;21:1590–600.PubMed 
    Article 

    Google Scholar 
    Chen Y, Yan F, Chai Y, Liu H, Kolter R, Losick R, et al. Biocontrol of tomato wilt disease by Bacillus subtilis isolates from natural environments depends on conserved genes mediating biofilm formation. Environ Microbiol. 2013;15:848–64.PubMed 
    Article 

    Google Scholar 
    Elphinstone J, Hennessy J, Wilson J, Stead D. Sensitivity of different methods for the detection of Ralstonia solanacearum in potato tuber extracts. EPPO Bull. 1996;26:663–78.Article 

    Google Scholar 
    Schonfeld J, Heuer H, van Elsas JD, Smalla K. Specific and sensitive detection of Ralstonia solanacearum in soil on the basis of PCR amplification of fliC fragments. Appl Environ Microbiol. 2003;69:7248–56.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wei Z, Yang X, Yin S, Shen Q, Ran W, Xu Y. Efficacy of Bacillus-fortified organic fertiliser in controlling bacterial wilt of tomato in the field. Appl Soil Ecol. 2011;48:152–9.Article 

    Google Scholar 
    Cardenas E, Wu WM, Leigh MB, Carley J, Carroll S, Gentry T, et al. Significant association between sulfate-reducing bacteria and uranium-reducing microbial communities as revealed by a combined massively parallel sequencing-indicator species approach. Appl Environ Microbiol. 2010;76:6778–86.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gu Y, Wei Z, Wang X, Friman V-P, Huang J, Wang X, et al. Pathogen invasion indirectly changes the composition of soil microbiome via shifts in root exudation profile. Biol Fertil Soils. 2016;52:997–1005.CAS 
    Article 

    Google Scholar 
    Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011;27:2194–2200.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Edgar RC UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv. 2016. https://doi.org/10.1101/081257.Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Olsen SR, Cole CV, Watanabe FS, Dean L. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. Circ no. 939. Washington, DC: United States Department of Agriculture; 1954.Heuer H, Krsek M, Baker P, Smalla K, Wellington E. Analysis of actinomycete communities by specific amplification of genes encoding 16S rRNA and gel-electrophoretic separation in denaturing gradients. Appl Environ Microbiol. 1997;63:3233–41.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Community Ecol package. 2007;10:719.
    Google Scholar 
    Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:1–18.Article 

    Google Scholar 
    Matsumoto H, Fan X, Wang Y, Kusstatscher P, Duan J, Wu S, et al. Bacterial seed endophyte shapes disease resistance in rice. Nat Plants. 2021;7:60–72.CAS 
    PubMed 
    Article 

    Google Scholar 
    Bardgett RD, Caruso T. Soil microbial community responses to climate extremes: resistance, resilience and transitions to alternative states. Proc R Soc Lond Ser B. 2020;375:20190112.CAS 

    Google Scholar 
    Mendes R, Kruijt M, de Bruijn I, Dekkers E, van der Voort M, Schneider JH, et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science. 2011;332:1097–100.CAS 
    PubMed 
    Article 

    Google Scholar 
    Raaijmakers JM, Mazzola M. Soil immune responses. Science. 2016;352:1392–3.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gu Y, Wang X, Yang T, Friman VP, Geisen S, Wei Z, et al. Chemical structure predicts the effect of plant-derived low molecular weight compounds on soil microbiome structure and pathogen suppression. Funct Ecol. 2020;34:2158–69.Article 

    Google Scholar 
    Burdon J, Chilvers G. Host density as a factor in plant disease ecology. Annu Rev Phytopathol. 1982;20:143–66.Article 

    Google Scholar 
    Rosenfeld M, Gibson RL, McNamara S, Emerson J, Burns JL, Castile R, et al. Early pulmonary infection, inflammation, and clinical outcomes in infants with cystic fibrosis. Pediatr Pulmonol. 2001;32:356–66.CAS 
    PubMed 
    Article 

    Google Scholar 
    Li J-G, Ren G-D, Jia Z-J, Dong Y-H. Composition and activity of rhizosphere microbial communities associated with healthy and diseased greenhouse tomatoes. Plant Soil. 2014;380:337–47.CAS 
    Article 

    Google Scholar 
    Liu X, Zhang S, Jiang Q, Bai Y, Shen G, Li S, et al. Using community analysis to explore bacterial indicators for disease suppression of tobacco bacterial wilt. Sci Rep. 2016;6:36773.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Filion M, Hamelin RC, Bernier L, St-Arnaud M. Molecular profiling of rhizosphere microbial communities associated with healthy and diseased black spruce (Picea mariana) seedlings grown in a nursery. Appl Environ Microbiol. 2004;70:3541–51.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gu Y, Dong K, Geisen S, Yang W, Yan Y, Gu D, et al. The effect of microbial inoculant origin on the rhizosphere bacterial community composition and plant growth-promotion. Plant Soil. 2020;452:105–17.CAS 
    Article 

    Google Scholar 
    Jiang G, Wang N, Zhang Y, Wang Z, Zhang Y, Yu J, et al. The relative importance of soil moisture in predicting bacterial wilt disease occurrence. Soil Ecol Lett. 2021;3:356–66.Article 

    Google Scholar 
    Mendes R, Raaijmakers JM. Cross-kingdom similarities in microbiome functions. ISME J 2015;9:1905–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dhaouadi S, Rouissi W, Mougou-Hamdane A, Nasraoui B. Evaluation of biocontrol potential of Achromobacter xylosoxidans against Fusarium wilt of melon. Eur J Plant Pathol. 2018;154:179–88.Article 

    Google Scholar 
    Halet D, Defoirdt T, Van Damme P, Vervaeren H, Forrez I, Van de Wiele T, et al. Poly-beta-hydroxybutyrate-accumulating bacteria protect gnotobiotic Artemia franciscana from pathogenic Vibrio campbellii. FEMS Microbiol Ecol. 2007;60:363–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Fujiwara K, Iida Y, Someya N, Takano M, Ohnishi J, Terami F, et al. Emergence of antagonism against the pathogenic fungus Fusarium oxysporum by interplay among non-antagonistic bacteria in a hydroponics using multiple parallel mineralization. J Phytopathol. 2016;164:853–62.CAS 
    Article 

    Google Scholar 
    Garbeva P, Silby MW, Raaijmakers JM, Levy SB, de Boer W. Transcriptional and antagonistic responses of Pseudomonas fluorescens Pf0-1 to phylogenetically different bacterial competitors. ISME J. 2011;5:973–85.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sato Y, Willis BL, Bourne DG. Successional changes in bacterial communities during the development of black band disease on the reef coral, Montipora hispida. ISME J. 2010;4:203–14.PubMed 
    Article 

    Google Scholar 
    Glasl B, Herndl GJ, Frade PR. The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance. ISME J. 2016;10:2280–92.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016;10:655–64.CAS 
    PubMed 
    Article 

    Google Scholar 
    Badri DV, Chaparro JM, Zhang R, Shen Q, Vivanco JM. Application of natural blends of phytochemicals derived from the root exudates of Arabidopsis to the soil reveal that phenolic-related compounds predominantly modulate the soil microbiome. J Biol Chem. 2013;288:4502–12.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Afzal I, Shinwari ZK, Sikandar S, Shahzad S. Plant beneficial endophytic bacteria: mechanisms, diversity, host range and genetic determinants. Microbiol Res. 2019;221:36–49.CAS 
    PubMed 
    Article 

    Google Scholar 
    Swanson JK, Montes L, Mejia L, Allen C. Detection of Latent Infections of Ralstonia solanacearum Race 3 Biovar 2 in geranium. Plant Dis. 2007;91:828–34.PubMed 
    Article 

    Google Scholar  More

  • in

    Development of microbial communities in biofilm and activated sludge in a hybrid reactor

    Bacterial community compositionIn order to study the microbial structure of the biofilm and activated sludge that were developing in the IFAS-MBSBBR reactor, a total of 15 samples were taken at intervals during an experiment lasting 573 days. The microbiome of both environments was described at the phylum and genus levels. A total of 26 bacterial phyla and 783 bacterial genera were identified. The most numerous phyla and genera in the biofim and activated sludge samples are presented in in Figs. 1 and 2. Both in the biofilm and the activated sludge, the most numerous phyla were Proteobacteria, with respective mean abundances of 39.3% ± 9.0 and 40.8% ± 8.2, and Bacteroidota, with respective mean abundances of 14.2% ± 4.9 and 26.1% ± 13.7. Additionally, the phylum Chloroflexi was rather abundant in the biofilm (with a mean abundance of 13.9 ± 8.1), while Actinobacteriota and Patescibacteria were relatively abundant in the activated sludge (with mean abundances of 9.0% ± 9.6 and 7.5% ± 8.1, respectively). STAMP analysis identified significant overrepresentations of Chloroflexi, Acidobacteriota, and Nitrospirota in biofilm and of Firmicutes in activated sludge.Figure 1Relative abundance (%) of the most prevalent phyla in the biofilm and activated sludge samples in general, as the mean values of relative abundance from all biofilm and activated sludge samples (A), and in each individual sample (B). The graph shows only phyla which contributed more than 0.5% to the total bacterial community in at least one sample. The abundance of the remaining phyla was summed and labelled as “other”.Full size imageFigure 2Relative abundance (%) of the most prevalent genera in the biofilm and activated sludge samples in general, as the mean values of relative abundance from all biofilm and activated sludge samples (A), and in each individual sample (B). The graph shows only genera which contributed more than 1.5% to the total bacterial community in at least one sample. The abundance of the remaining genera was summed and labelled as “other”.Full size imageIn both environments, the abundances of various groups of bacteria changed over time. In the biofilm, the abundance of Proteobacteria and Actinobacteria gradually decreased, while that of Chloroflexi increased. In the activated sludge, the changes in abundance were larger and more rapid, and the abundance of Bacteroidota changed to the largest extent, ranging from 12.7% after 42 days of reactor operation to 52.3% after 110 days, when it was the predominant phylum. The abundance of Patescibacteria also changed substantially: its abundance was highest on the 78th, 205th and 447th days of the process, reaching values of 20.1%, 11.0%, and 7.2%, respectively. Similar changes took place in the abundance of Armatimonadota, which reached 11.4% and 7.6% on the 547th and 573th day, but did not exceed 0.1% in the samples taken at other times.At the genus level, the less abundant genera (each  More

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    A complex story of groundwater abstraction and ecological threats to the Doñana National Park World Heritage Site

    To the Editor — It is widely appreciated that the world’s wetlands provide important ecosystem services including critical biodiversity, stores of carbon and strong cultural links to people. Yet wetlands are disappearing at an alarming rate due to diversion and abstraction of water, to conversion to agricultural land and to pollution. In response, there has been a major commitment to conserve and restore wetlands worldwide, including more than 2,400 sites on the territories of 172 Contracting Parties of the Convention on Wetlands (Ramsar Sites), covering more than 2.5 million square kilometres. Some wetlands, such as Doñana in southern Spain, are also World Heritage sites to protect their natural and cultural values. The Ramsar Convention and UNESCO World Heritage Convention strongly support the rights of non-governmental organizations to appraise the status and management of designated sites and welcome reports of threats to site integrity. However, such claims should be substantiated by all the available scientific evidence. More

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    Phycobilisome light-harvesting efficiency in natural populations of the marine cyanobacteria Synechococcus increases with depth

    Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Goericke, R. & Welschmeyer, N. A. The marine prochlorophyte Prochlorococcus contributes significantly to phytoplankton biomass and primary production in the Sargasso Sea. Deep Res. 40, 2283–2294 (1993).Article 

    Google Scholar 
    Liu, H., Nolla, H. A. & Campbell, L. Prochlorococcus growth rate and contribution to primary production in the equatorial and subtropical North Pacific Ocean. Aquat. Microb. Ecol. 12, 39–47 (1997).Article 

    Google Scholar 
    Huang, S. et al. Novel lineages of prochlorococcus and synechococcus in the global oceans. ISME J. 6, 285–297 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ting, C. S., Rocap, G., King, J. & Chisholm, S. W. Cyanobacterial photosynthesis in the oceans: the origins and significance of divergent light-harvesting strategies. Trends Microbiol. 10, 134–142 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Barlow, A. Photosynthetic characteristics of phycoerythrin-containing marine Synechococcus spp. Arctic 22, 63–74 (1985).
    Google Scholar 
    Yeh, S. W. et al. Role of phycoerythrin in marine picoplankton synechococcus spp. Science 234, 1422–1424 (1986).CAS 
    PubMed 
    Article 

    Google Scholar 
    Giovannoni, S. J. & Vergin, K. L. Seasonality in ocean microbial communities. Science 335, 671–676 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Carlson, D. F., Fredj, E. & Gildor, H. The annual cycle of vertical mixing and restratification in the Northern Gulf of Eilat/Aqaba (Red Sea) based on high temporal and vertical resolution observations. Deep Res. Part I Oceanogr. Res. Pap. 84, 1–17 (2014).Article 

    Google Scholar 
    Larkum, A. W. D. & Barrett, J. Light-harvesting processes in algae. Adv. Bot. Res. 10, 1–219 (1983).CAS 
    Article 

    Google Scholar 
    Bibby, T. S., Mary, I., Nield, J., Partensky, F. & Barber, J. Low-light-adapted Prochlorococcus species possess specific antennae for each photosystem. Nature 424, 1051–1054 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bibby, T. S., Nield, J., Chen, M., Larkum, A. W. D. & Barber, J. Structure of a photosystem II supercomplex isolated from Prochloron didemni retaining its chlorophyll a/b light-harvesting system. Proc. Natl Acad. Sci. USA 100, 9050–9054 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Palenik, B. Chromatic adaptation in marine Synechococcus strains. Appl. Environ. Microbiol. 67, 991–994 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kana, T. M. & Glibert, P. M. Effect of irradiances up to 2000 μE m-2 s-1 on marine Synechococcus WH7803-I. Growth, pigmentation, and cell composition. Deep Sea Res. Part A Oceanogr. Res. Pap. 34, 479–495 (1987).CAS 
    Article 

    Google Scholar 
    Six, C., Ratin, M., Marie, D. & Corre, E. Marine Synechococcus picocyanobacteria: light utilization across latitudes. Proc. Natl Acad. Sci. USA 118, 1–11 (2021).Article 
    CAS 

    Google Scholar 
    Perry, M. J., Talbot, M. C. & Alberte, R. S. Photoadaption in marine phytoplankton: response of the photosynthetic unit. Mar. Biol. 62, 91–101 (1981).Mauzerall, D. & Greenbaum, N. L. The absolute size of a photosynthetic unit. BBA Bioenerg. 974, 119–140 (1989).CAS 
    Article 

    Google Scholar 
    Sanfilippo, J. E., Garczarek, L., Partensky, F. & Kehoe, D. M. Chromatic acclimation in cyanobacteria: a diverse and widespread process for optimizing photosynthesis. Annu. Rev. Microbiol. 73, 407–433 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Keren, N. & Paltiel, Y. Photosynthetic energy transfer at the quantum/classical border. Trends Plant Sci. 23, 497–506 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kolodny, Y. et al. Marine cyanobacteria tune energy transfer efficiency in their light‐harvesting antennae by modifying pigment coupling. FEBS J. https://doi.org/10.1111/febs.15371 (2020).Wientjes, E., Van Amerongen, H. & Croce, R. Quantum yield of charge separation in photosystem II: functional effect of changes in the antenna size upon light acclimation the migration of LHCII from PSII to PSI has. J. Phys. Chem. B 117, 51 (2013).Article 
    CAS 

    Google Scholar 
    Chenu, A. et al. Light adaptation in phycobilisome antennas: influence on the rod length and structural arrangement. J. Phys. Chem. B 121, 9196–9202 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Falkowski, P. G., Lin, H. & Gorbunov, M. Y. What limits photosynthetic energy conversion efficiency in nature? Lessons from the oceans. Philos. Trans. R. Soc. B Biol. Sci. 372, 2–8 (2017).Article 
    CAS 

    Google Scholar 
    Gorbunov, M. Y. & Falkowski, P. G. Using chlorophyll fluorescence to determine the fate of photons absorbed by phytoplankton in the world’s oceans. Ann. Rev. Mar. Sci. 14, 367–393 (2021).
    Google Scholar 
    Govindjee, Hammond, J. H. & Merkelo, H. Primary events, energy transfer, and reactions in photosynthetic events: lifetime of the excited state in vivo: II. Bacteriochlorophyll in photosynthetic bacteria at room temperature. Biophys. J. 12, 809 (1972).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Biggins, J. & Bruce, D. Regulation of excitation energy transfer in organisms containing phycobilins. Photosynth. Res. 20, 1–34 (1989).CAS 
    PubMed 
    Article 

    Google Scholar 
    Roach, T. & Krieger-Liszkay, A. Regulation of photosynthetic electron transport and photoinhibition. Curr. Protein Pept. Sci. 15, 351–362 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Govindjee, U. Non-Photochemical Quenching and Energy Dissipation in Plants, Algae, and Cyanobacteria (Springer Netherlands, 2014).
    Google Scholar 
    Kirilovsky, D. Photoprotection in cyanobacteria: the orange carotenoid protein (OCP)-related non-photochemical-quenching mechanism. Photosynth. Res. 93, 7–16 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lin, H. et al. The fate of photons absorbed by phytoplankton in the global ocean. Science 351, 264–267 (2016).Croce, R. & Van Amerongen, H. Light-harvesting and structural organization of photosystem II: from individual complexes to thylakoid membrane. J. Photochem. Photobiol. B Biol. 104, 142–153 (2011).CAS 
    Article 

    Google Scholar 
    Rahav, E. et al. Heterotrophic and autotrophic contribution to dinitrogen fixation in the Gulf of Aqaba. Mar. Ecol. Prog. Ser. 522, 67–77 (2015).CAS 
    Article 

    Google Scholar 
    Reiss, Z. & Hottinger, L. The Gulf of Aqaba (Springer-Verlag, 1984).Genin, A., Lazar, B. & Brenner, S. Vertical mixing and coral death in the red sea following the eruption of Mount Pinatubo. Nature 377, 507–510 (1995).CAS 
    Article 

    Google Scholar 
    Labiosa, R. G., Arrigo, K. R., Genin, A., Monismith, S. G. & Van Dijken, G. The interplay between upwelling and deep convective mixing in determining the seasonal phytoplankton dynamics in the Gulf of Aqaba: evidence from SeaWiFS and MODIS. Limnol. Oceanogr. 48, 2355–2368 (2003).Article 

    Google Scholar 
    Zarubin, M., Lindemann, Y. & Genin, A. The dispersion-confinement mechanism: phytoplankton dynamics and the spring bloom in a deeply-mixing subtropical sea. Prog. Oceanogr. 155, 13–27 (2017).Article 

    Google Scholar 
    Lindell, D. & Post, A. F. Ultraphytoplankton succession is triggered by deep winter mixing in the Gulf of Aqaba (Eilat), Red Sea. Limnol. Oceanogr. 40, 1130–1141 (1995).Article 

    Google Scholar 
    Suggett, D. J. et al. Nitrogen and phosphorus limitation of oceanic microbial growth during spring in the Gulf of Aqaba. Aquat. Microb. Ecol. 56, 227–239 (2009).Article 

    Google Scholar 
    Post, A. F. et al. Long term seasonal dynamics of Synechococcus population structure in the Gulf of Aqaba, Northern Red Sea. Front. Microbiol. 2, 131 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sherman, J., Gorbunov, M. Y., Schofield, O. & Falkowski, P. G. Photosynthetic energy conversion efficiency in the West Antarctic Peninsula. Limnol. Oceanogr. 65, 2912–2925 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yoo, Y. D. et al. Mixotrophy in the marine red-tide cryptophyte Teleaulax amphioxeia and ingestion and grazing impact of cryptophytes on natural populations of bacteria in Korean coastal waters. Harmful Algae 68, 105–117 (2017).PubMed 
    Article 

    Google Scholar 
    Marie, D., Partensky, F., Jacquet, S. & Vaulot, D. Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR Green I. Appl. Environ. Microbiol. 63, 186–193 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Brody, S. S. & Rabinowitch, E. Excitation lifetime of photosynthetic pigments in vitro and in vivo. Science 125, 555 (1979).Article 

    Google Scholar 
    Six, C., Thomas, J. C., Brahamsha, B., Lemoine, Y. & Partensky, F. Photophysiology of the marine cyanobacterium Synechococcus sp. WH8102, a new model organism. Aquat. Microb. Ecol. 35, 17–29 (2004).Article 

    Google Scholar 
    Krumova, S. B. et al. Monitoring photosynthesis in individual cells of Synechocystis sp. PCC 6803 on a picosecond timescale. Biophys. J. 99, 2006–2015 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tian, L. et al. Picosecond kinetics of light harvesting and photoprotective quenching in wild-type and mutant phycobilisomes isolated from the cyanobacterium Synechocystis PCC 6803. Biophys. J. 102, 1692–1700 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bhatti, A. F., Kirilovsky, D., van Amerongen, H. & Wientjes, E. State transitions and photosystems spatially resolved in individual cells of the cyanobacterium Synechococcus elongatus. Plant Physiol. 186, 569–580 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Adir, N., Bar-Zvi, S. & Harris, D. The amazing phycobilisome. Biochim. Biophys. Acta Bioenerg. 1861, 148047 (2020).Anderson, J. M. & Andersson, B. The dynamic photosynthetic membrane and regulation of solar energy conversion. Trends Biochem. Sci. 13, 351–355 (1988).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mackey, K. R. M., Post, A. F., McIlvin, M. R. & Saito, M. A. Physiological and proteomic characterization of light adaptations in marine Synechococcus. Environ. Microbiol. https://doi.org/10.1111/1462-2920.13744 (2017).Article 
    PubMed 

    Google Scholar 
    Mendoza-Arenas, J. J. et al. Transport enhancement from incoherent coupling between one-dimensional quantum conductors. New J. Phys. 16, 053016 (2014).Campbell, D., Hurry, V., Clarke, A. K., Gustafsson, P. & Quist, G. O. Chlorophyll fluorescence analysis of cyanobacterial photosynthesis and acclimation. Microbiol. Mol. Biol. Rev. 62, 667–683 (1998).Ogawa, T., Misumi, M. & Sonoike, K. Estimation of photosynthesis in cyanobacteria by pulse-amplitude modulation chlorophyll fluorescence: problems and solutions. Photosynth. Res. 133, 63–73 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kolber, Z. S., Prášil, O. & Falkowski, P. G. Measurements of variable chlorophyll fluorescence using fast repetition rate techniques: defining methodology and experimental protocols. Biochim. Biophys. Acta Bioenerg. 1367, 88–106 (1998).CAS 
    Article 

    Google Scholar 
    Kolber, Z. & Falkowski, P. G. Use of active fluorescence to estimate phytoplankton photosynthesis in situ. Limnol. Oceanogr. 38, 1646–1665 (1993).CAS 
    Article 

    Google Scholar 
    Siegel, D. A. et al. Regional to global assessments of phytoplankton dynamics from the SeaWiFS mission. Remote Sens. Environ. 135, 77–91 (2013).Article 

    Google Scholar 
    Gregg, W. W. & Rousseaux, C. S. Global ocean primary production trends in the modern ocean color satellite record (1998-2015). Environ. Res. Lett. 14, 124011 (2019).Kulk, G. et al. Primary production, an index of climate change in the ocean: satellite-based estimates over two decades. Remote Sens. 12, 826 (2020).Van De Poll, W. H. et al. Phytoplankton chlorophyll a biomass, composition, and productivity along a temperature and stratification gradient in the northeast Atlantic Ocean. Biogeosciences 10, 4227–4240 (2013).Article 
    CAS 

    Google Scholar 
    Agusti, S., Lubián, L. M., Moreno-Ostos, E., Estrada, M. & Duarte, C. M. Projected changes in photosynthetic picoplankton in a warmer subtropical ocean. Front. Mar. Sci. 5, 1–16 (2019).Article 

    Google Scholar 
    Capotondi, A., Alexander, M. A., Bond, N. A., Curchitser, E. N. & Scott, J. D. Enhanced upper ocean stratification with climate change in the CMIP3 models. J. Geophys. Res. Ocean. 117, 1–23 (2012).Article 

    Google Scholar 
    Li, G. et al. Increasing ocean stratification over the past half-century. Nat. Clim. Chang. 10, 1116–1123 (2020).Article 

    Google Scholar 
    Kolodny, Y. et al. Tuning quantum dots coupling using organic linkers with different vibrational modes. J. Phys. Chem. C 124, 16159–16165 (2020).CAS 
    Article 

    Google Scholar  More