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    A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north‐central United States

    The surveyed, rainfed commercial soybean fields were spread across the U.S. north-central region (Supplementary Fig. S1 online) with a latitudinal gradient evident for maturity group (MG). The number of fields (n) was distributed evenly across the three years (2014: n = 812, 2015: n = 960, 2016: n = 966). Among the 2738 fields, 833 (or 30.4%) were sprayed with foliar fungicides. Out of the 833 fields sprayed with foliar fungicides, 623 (74.8%) had also been sprayed with foliar insecticides.A t-test estimate of the yield difference between all fields sprayed with foliar fungicides and those which were not was 0.46 t/ha (95% confidence interval [CI] of 0.39 to 0.52 t/ha). When t-tests were applied to fields within TEDs (the 12 TEDs with the most fields), half of the 95% CIs included zero, indicative of possibly no yield increase due to foliar fungicides over unsprayed fields in those TEDs (Supplementary Fig. S2 online). A linear mixed model with random slopes and intercepts for the fungicide effect within TEDs returned an estimated yield gain of 0.33 t/ha due to foliar fungicide use. A simpler model without random slopes for foliar fungicide was a worse fit to the data. Together these basic tests were indicative of heterogenous effects concerning foliar fungicides and yield gain, implying other global (regional) and local (field specific) conditions may be involved as factors.A tuned random forest (RF) model fitted to the entire dataset (all 2738 observations) overpredicted soybean yield at low actual yields, and underpredicted at the high-yield end (Supplementary Fig. S3 online). However, as 99% of the residual values were less than or equal to |0.25 t/ha| which corresponded to less than 7% of the average yield, we proceeded with the interpretation of the fit RF model. The mean predicted soybean yield (global average) was 3.79 t/ha (minimum = 1.13 t/ha, maximum = 6.02 t/ha, standard deviation = 0.81 t/ha, root mean squared error between the observed and predicted yields = 0.1 t/ha).At the global model level, location (latitude; a surrogate for other unmeasured variables) and sowing date (day of year from Jan 01) were the two variables most associated with yield (Fig. 1), consistent with the central importance of early planting to soybean yield5,13. Soil-related properties (pH and organic matter content of the topsoil) were also associated with yield (Fig. 1). Management-related variables such as foliar fungicide, insecticide and herbicide applications were of intermediate importance, and other management variables (row spacing, seed treatments, starter fertilizer) were on the lower end of the importance spectrum in predicting soybean yield (Fig. 1). Insecticide and fungicide seed treatments were poorly associated with soybean yield increases as has been previously shown8,40. The relatively lower importance of row spacing is consistent with previous analyses of this variable from soybean grower data6. The dataset we analyzed did not contain enough observations to include artificial drainage as a variable, which has been shown to influence soybean yield, presumably by allowing earlier sowing14.Figure 1Importance of management-based variables in a random forest model predicting soybean yield. Feature importance was measured as the ratio of model error, after permuting the values of a feature, to the original model error. A predictor was unimportant if the ratio was 1. Points are the medians of the ratio over all the permutations (repeated 20 times). The bars represent the range between the 5% and 95% quantiles. Sowing date was the number of days from Jan 01. Growing degree days and the aridity index were annualized categorical constructs used within the definition of technology extrapolation domains (TEDs). Foliar fungicide or insecticide use, seed treatment use, starter fertilizer use, lime and manure applications were all binary variables for the use (or not) of the practice. Iron deficiency was likewise binary (symptoms were observed or not). Topsoil texture, plant available water holding capacity in the rooting zone, row spacing, and herbicide program were categorical variables with five, seven, five, and four levels, respectively.Full size imageThe strongest pairwise interactions included that between sowing date and latitude. Delayed sowing at higher latitudes decreased yield by about 1 t/ha relative to the highest yielding fields sown early in the more southerly locations (Supplementary Fig. S4 online). Further examination of the interactions showed that the yield difference between sprayed and unsprayed fields increased with later sowing, indicative of a greater fungicide benefit in later-planted fields (Fig. 2). This would seem to conflict with the results of a recent meta-analysis in which soybean yields responded better when foliar fungicides were applied to early-planted fields27, but in that study there was also the confounding effect of higher-than-average rainfall between sowing and the R3 growth stage. With respect to latitude, the global difference in yield between sprayed and unsprayed fields decreased as one moved further north (Fig. 2), suggesting that foliar fungicides were of more benefit when applied to the more southerly located fields, which do tend to experience more or prolonged conditions conducive to foliar diseases than the northern fields22,24.Figure 2Two-way partial dependence plots of the global effects of (i) foliar fungicide use and sowing date (left panel), and (ii) foliar fungicide use and latitude (right panel) on soybean yield. The black plotted curves are the yield differences between fields that were sprayed or not sprayed with foliar fungicides. Smoothed versions of the curves are shown in blue.Full size imageFocusing on model interpretation at the local level, we examined the Shapley φ values (see the “Methods” section for more information) associated with foliar fungicide applications for different subsets (s) and cohorts (c) of fields within the data (see Supplementary Table S1 online). The 1st subset (s1) was comprised of the 20 highest-yielding fields among those sprayed with foliar fungicides (s1c1) and the 20 highest-yielding fields among those which were not sprayed (s1c2) in each of the 12 technology extrapolation domains (TEDs) in the data matrix with adequate numbers of fields for comparisons (see also Supplementary Table S2 online; Supplementary Fig. S5 online maps the field locations within these 12 TEDs). A TED is a region (not necessarily spatially contiguous) with similar biophysical properties41. Predicted yields within these cohorts were mainly above the global average of 3.79 t/ha, except in TED 602303 (Fig. 3), which corresponded to fields in North Dakota (Supplementary Fig. S5). In most cases Shapley φ values for foliar fungicide use exhibited a positive contribution to the yield above the global average. If these cohorts of fields represented high-yielding environments within each TED, then foliar fungicide sprays contributed positively up to 0.3 t/ha in the yield increase above the global average in s1c1. However, among high-yielding fields in s1c2, the penalty for not spraying was less than 0.1 t/ha. This finding supports the contention that fungicide sprays are most worthwhile in high-yielding environments. Supplementary Fig. S6 online complements Fig. 3 by summarizing the Shapley φ values in another visual format. The overall mean predicted yield for the unsprayed (s1c2) fields was slightly higher (by 0.1 t/ha) than that for the sprayed (s1c1) fields (Supplementary Fig. S6 online). This difference may have been driven by the higher variability in yields among the two cohorts (particularly for TEDs 403603, 602303, 403703, and 303603), or underlying differences in other management factors. Also, the number of sprayed fields in each of these four TEDs was at the target sampling boundary of 20 fields per TED (Supplementary Table S2 online).Figure 3Shapley phi values attributed to foliar fungicide use for two cohorts of fields within the 12 technology extrapolation domains (TEDs) with the most fields. Within each TED, the cohorts are the 20 highest-yielding fields among those sprayed with foliar fungicides and the 20 highest-yielding fields among those which were unsprayed.Full size imageThe Shapley φ values for fungicide use were well-separated among the four cohorts of fields of s2 (Fig. 4, Supplementary Table S1 online). The fields within s2 were selected across the entire dataset and not by TED membership. The lowest-yielding fields (s2c2 & s2c4) were all below the global yield average, whereas the converse was true of the highest-yielding fields (s2c1 & s2c3). Among the lowest-yielding fields, foliar fungicides were mainly associated with a positive, but less than 0.2 t/ha, effect on yield (s2c2), and other factors were responsible for dropping a field’s yield to below the global average. Amongst the highest-yielding fields (s2c1), foliar fungicides were associated with between 0.15 and 0.35 t/ha of the yield above the global average. These Shapley φ values for the contribution of foliar fungicides are consistent with estimates of the yield response to foliar fungicides from a meta-analytic perspective27. Given that the individual yields in s2c1 & s2c3 were 1 to 2 t/ha above the global average, other location-driven factors such as early sowing (Fig. 1) were the larger drivers of yield in these cases. However, there was only a negligible or small ( More

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    Palaeoclimate has a major effect on the diversity of endemic species in the hotspot of mountain biodiversity in Tajikistan

    1.Lohr, T. A Short Story About the Geological History of the Pamir (University of Mining and Technology Freiberg, 2001).
    Google Scholar 
    2.Safarov, N. National Strategy and Action Plan on Conservation and Sustainable Use of Biodiversity (Governmental Working Group of the Republic of Tajikistan, 2003).
    Google Scholar 
    3.Nowak, A., Nowak, S. & Nobis, M. Distribution patterns, ecological characteristic and conservation status of endemic plants of Tadzhikistan: A global hotspot of diversity. J. Nat. Conserv. 19, 296–305 (2011).Article 

    Google Scholar 
    4.Nowak, A. et al. Red List of vascular plants of Tajikistan: The core area of the Mountains of Central Asia global biodiversity hotspot. Sci. Rep. 10, 6235 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Bagheri, A., Maassoumi, A. A., Rahiminejad, M. R., Brassac, J. & Blattner, F. R. Molecular phylogeny and divergence times of Astragalus section Hymenostegis: An analysis of a rapidly diversifying species group in Fabaceae. Sci. Rep. 7, 14033 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    6.Mittermeier, R. A. et al. Hotspots Revisited: Earth’s Biologically Richest and Most Threatened Terrestrial Ecoregions. (Conservation International, 2005).7.Abramowski, U. et al. Pleistocene glaciations of Central Asia: Results from 10Be surface exposure ages of erratic boulders from the Pamir (Tajikistan), and the Alay-Turkestan range (Kyrgyzstan). Quat. Sci. Rev. 25, 1080–1096 (2006).ADS 
    Article 

    Google Scholar 
    8.Cowling, R. M. & Lombard, A. T. Heterogeneity, speciation/extinction history and climate: Explaining regional plant diversity patterns in the Cape Floristic Region. Divers. Distrib. 8, 163–179 (2002).Article 

    Google Scholar 
    9.Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).Article 

    Google Scholar 
    10.López-Pujol, J., Zhang, F. M., Sun, H. Q., Ying, T. S. & Ge, S. Centres of plant endemism in China: Places for survival or for speciation?. J. Biogeogr. 38, 1267–1280 (2011).Article 

    Google Scholar 
    11.Chen, X.-Y. & He, F. Speciation and endemism under the model of island biogeography. Ecology 90, 39–45 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Bruchmann, I. & Hobohm, C. Factors that create and increase endemism. In Endemism in Vascular Plants (ed. Hobohm, C.) 51–68 (Springer, 2014).Chapter 

    Google Scholar 
    13.Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl. Acad. Sci. U. S. A. 97, 9115–9120 (2000).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Vetaas, O. R. & Grytnes, J. A. Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal. Glob. Ecol. Biogeogr. 11, 291–301 (2002).Article 

    Google Scholar 
    15.Mucina, L. & Wardell-Johnson, G. W. Landscape age and soil fertility, climatic stability, and fire regime predictability: Beyond the OCBIL framework. Plant Soil 341, 1–23 (2011).CAS 
    Article 

    Google Scholar 
    16.Tzedakis, P. C. Museums and cradles of Mediterranean biodiversity. J. Biogeogr. 36, 1033–1034 (2009).Article 

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

    Google Scholar 
    18.Noroozi, J., Pauli, H., Grabherr, G. & Breckle, S. W. The subnival-nival vascular plant species of Iran: A unique high-mountain flora and its threat from climate warming. Biodivers. Conserv. 20, 1319–1338 (2011).Article 

    Google Scholar 
    19.Pauli, H., Gottfried, M., Dirnböck, T., Dullinger, S. & Grabherr, G. Assessing the long-term dynamics of endemic plants at summit habitats. In Alpine Biodiversity in Europe (eds Nagy, L. et al.) 195–207 (Springer, 2003).Chapter 

    Google Scholar 
    20.Agakhanjanz, O. & Breckle, S. W. Origin and evolution of the mountain flora in middle asia and neighbouring mountain regions. In Arctic and Alpine Biodiversity: Patterns, Causes and Ecosystem Consequences Ecological Studies (Analysis and Synthesis) Vol. 113 (eds Chapin, F. S. & Körner, C.) 63–80 (Springer, 1995).
    Google Scholar 
    21.Noroozi, J., Akhani, H. & Willner, W. Phytosociological and ecological study of the high alpine vegetation of Tuchal mountains (Central Alborz, Iran). Phytocoenologia 40, 293–321 (2010).Article 

    Google Scholar 
    22.Goldblatt, P. & Manning, J. C. Plant Diversity of the Cape Region of Southern Africa. Ann. Mo. Bot. Gard. 89, 281–302 (2002).Article 

    Google Scholar 
    23.Bond, P. & Goldblatt, P. Plants of the Cape fora: a descriptive catalogue. J. S Afr. Bot. Suppl. 13, 1–455 (1984).
    Google Scholar 
    24.Panda, R. M., Behera, M. D., Roy, P. S. & Biradar, C. Energy determines broad pattern of plant distribution in Western Himalaya. Ecol. Evol. 7, 10850–10860 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Nowak, A., Nowak, S., Nobis, M. & Nobis, A. A report on the conservation status of segetal weeds in Tajikistan. Weed Res. 54, 635–648 (2014).Article 

    Google Scholar 
    26.Nobis, M., Gudkova, P. D., Nowak, A., Sawicki, J. & Nobis, A. A synopsis of the genus Stipa (Poaceae) in Middle Asia, including a key to species identyfication, an annoted checklist and phytogeographical analyses. Ann. Missouri Bot. Gard. 105, 1–63 (2020).Article 

    Google Scholar 
    27.Thompson, J. N. The Geographic Mosaic of coevolution (Chicago Univ Press, 2005).Book 

    Google Scholar 
    28.Thompson, J. N. Four central points about coevolution. Evol. Educ. Outreach 3, 7–13 (2010).Article 

    Google Scholar 
    29.Thrall, P. H., Hochberg, M. E., Burdon, J. J. & Bever, J. D. Coevolution of symbiotic mutualists and parasites in a community context. Trends Ecol. Evol. 22, 120–126 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Navarro-Fernández, C. M., Aroca, R. & Barea, J. M. Influence of arbuscular mycorrhizal fungi and water regime on the development of endemic Thymus species in dolomitic soils. Appl. Soil Ecol. 48, 31–37 (2011).Article 

    Google Scholar 
    31.Zubek, S., Nobis, M., Błaszkowski, J., Mleczko, P. & Nowak, A. Fungal root endophyte associations of plants endemic to the Pamir Alay Mountains of Central Asia. Symbiosis 54, 139–149 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Lambers, H., Chapin, F. S. III. & Pons, T. L. Plant Physiological Ecology (Springer, 2008).Book 

    Google Scholar 
    33.Lambers, H., Brundrett, M. C., Raven, J. A. & Hopper, S. D. Plant mineral nutrition in ancient landscapes: High plant species diversity on infertile soils is linked to functional diversity for nutritional strategies. Plant Soil 334, 11–31 (2010).CAS 
    Article 

    Google Scholar 
    34.Hopper, S. D. OCBIL theory: Towards an integrated understanding of the evolution, ecology and conservation of biodiversity on old, climatically buffered, infertile landscapes. Plant Soil 322, 49–86 (2009).CAS 
    Article 

    Google Scholar 
    35.Ellison, A. M. & Gotelli, N. J. Energetics and the evolution of carnivorous plants – Darwin’s ‘most wonderful plants in the world’. J. Exp. Bot. 60, 19–42 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Merckx, V., Bidartondo, M. I. & Hynson, N. A. Myco-heterotrophy: When fungi host plants. Ann. Bot. 104, 1255–1261 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Huang, B. H. et al. Differential genetic responses to the stress revealed the mutation-order adaptive divergence between two sympatric ginger species. BMC Genom. 19, 692 (2018).Article 
    CAS 

    Google Scholar 
    38.Turner, J. R. G., Gatehouse, C. M. & Core, C. A. Does solar energy control organic diversity? Butterflies moths and the British climate. Oikos 48, 195–205 (1987).Article 

    Google Scholar 
    39.Körner, C. Why are there global gradients in species richness? Mountains might hold the answer. Trends Ecol. Evol. 15, 513–514 (2000).Article 

    Google Scholar 
    40.Makhmadaliev, B., Novikov, V., Kayumov, A., Karimov, U. & Perdomo, M. National Action Plan of the Republic of Tajikistan for Climate Change Mitigation. (Tajik Met Service, 2003).41.Nedzvedskiy, A. P. Geologicheskoe stroenye. In Atlas Tajikskoi SSR (eds Narzikulov, I. K. & Stanyukovich, K. W.) 14–15 (Akademia Nauk Tajikskoi SSR, 1968).
    Google Scholar 
    42.Latipova, W. A. Kolichestvo osadkov. In Atlas Tajikskoi SSR (eds Narzikulov, I. K. & Stanyukovich, K. W.) 68–69 (Akademia Nauk Tajikskoi SSR, 1968).
    Google Scholar 
    43.Narzikulov, I. K. & Stanyukovich, K. W. Atlas Tajikskoi SSR. (Akademia Nauk Tajikskoi SSR, 1968).44.Rivas-Martínez, S., Rivas-Sáenz, S. & Penas, Á. Worldwide bioclimatic classification system. Glob. Geobot. 1, 1–638 (2011).
    Google Scholar 
    45.Djamali, M., Brewer, S., Breckle, S. W. & Jackson, S. T. Climatic determinism in phytogeographic regionalization: A test from the Irano-Turanian region, SW and Central Asia. Flora Morphol. Distrib. Funct. Ecol. Plants 207, 237–249 (2012).
    Google Scholar 
    46.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. I, Paprotnikoobraznye – Zlaki. (Izdatelstvo Akademii Nauk SSSR, 1957).47.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. II, Osokovye—Orkhidnye. (Izdatelstvo Akademii Nauk SSSR, 1963).48.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. III, Opekhovye—Gvozdichnye. (Izdatelstvo Nauka, 1968).49.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. IV, Rogolistnikovye—Rozotsvetnye. (Izdatelstvo Nauka, 1975).50.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. V, Krestotsvetne—Bobovye. (Izdatelstvo Nauka, 1978).51.Ovchinnikov, P. N. Flora Tadzhikskoi SSR. T. VI, Bobovye (rod Astragal). (Izdatelstvo Nauka, 1981).52.Kochkareva, T. F. Flora Tadzhikskoi SSR. T. VIII. Kermekovye—Podorozhnikovye. (Izdatelstvo Nauka, 1986).53.Kinzikaeva, G. K. Flora Tadzhikskoi SSR. T. IX. Marenovye – Slozhnotsvetnye. (Izdatelstvo Nauka, 1988).54.Rasulova, M. R. Flora Tadzhikskoi SSR. T. X, Slozhnotsvetnye. (Izdatelstvo Nauka, 1991).55.Grubov, V. I. Schlussbetrachtung zum Florenwerk ‘Rastenija Central’noj Azii’ [Die Pflanzen Zentralasiens] und die Begründung der Eigenständigkeit der mongolischen Flora. Feddes Repert. 121, 7–13 (2010).Article 

    Google Scholar 
    56.Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (http://srtm.csi.cgiar.org). (2008).57.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Zuur, A. F., Ieno, E. N. & Erik, H. W. G. Meesters A Beginner’s Guide to R (Springer, 2009).MATH 
    Book 

    Google Scholar 
    59.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 027–046 (2013).Article 

    Google Scholar 
    60.Wood, S. N. Generalized Additive Models An Introduction with R (Chapman and Hall/CRC, 2017).MATH 
    Book 

    Google Scholar 
    61.Therneau, T. & Atkinson, B. rpart: Recursive Partitioning and Regression Trees. R package version 4.1-13 (2018).62.De’ath, G. & Fabricius, K. E. Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81, 3178–3192 (2000).Article 

    Google Scholar  More

  • in

    Tetraploids expanded beyond the mountain niche of their diploid ancestors in the mixed-ploidy grass Festuca amethystina L.

    1.Otto, S. P. Adaptation, speciation and extinction in the Anthropocene. Proc. R. Soc. B 285, 20182047 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Moritz, C. & Agudo, R. The future of species under climate change: Resilience or decline?. Science 341, 504–508 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Parmesan, C. & Hanley, M. E. Plants and climate change: Complexities and surprises. Ann. Bot. 116, 849–864 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Soltis, P. S. & Soltis, D. E. The role of genetic and genomic attributes in the success of polyploids. Proc. Natl. Acad. Sci. U.S.A. 97, 7051–7057 (2000).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Barker, M. S., Husband, B. C. & Chris Pires, J. Spreading winge and flying high: The evolutionary importance of polyploidy after a century of study. Am. J. Bot. 103, 1139–1145 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Van De Peer, Y., Mizrachi, E. & Marchal, K. The evolutionary significance of polyploidy. Nat. Rev. Genet. 18, 411–424 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    7.Madlung, A. Polyploidy and its effect on evolutionary success: Old questions revisited with new tools. Heredity (Edinb) 110, 99–104 (2013).CAS 
    Article 

    Google Scholar 
    8.Soltis, D. E., Visger, C. J., Marchant, B. D. & Soltis, P. S. Polyploidy: Pitfalls and paths to a paradigm. Am. J. Bot. 103, 1146–1166 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Ramsey, J. Polyploidy and ecological adaptation in wild yarrow. Proc. Natl. Acad. Sci. U.S.A. 108, 7096–7101 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Oswald, B. P. & Nuismer, S. L. Neopolyploidy and diversification in Heuchera grossulariifolia. Evolution 65, 1667–1679 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Kolář, F., Čertner, M., Suda, J., Schönswetter, P. & Husband, B. C. Mixed-ploidy species: Progress and opportunities in polyploid research. Trends Plant Sci. https://doi.org/10.1016/j.tplants.2017.09.011 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Fowler, N. L. & Levin, D. A. Critical factors in the establishment of allopolyploids. Am. J. Bot. 103, 1236–1251 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Husband, B. C., Baldwin, S. J. & Suda, J. The incidence of polyploidy in natural plant populations: Major patterns and evolutionary processes. In Plant Genome Diversity 2: Physical Structure, Behaviour and Evolution of Plant Genomes (eds Leitch, I. et al.) 255–276 (Springer, 2013).Chapter 

    Google Scholar 
    14.Te Beest, M. et al. The more the better? The role of polyploidy in facilitating plant invasions. Ann. Bot. 109, 19–45 (2012).Article 

    Google Scholar 
    15.Watanabe, K. The cytogeography of the genus Eupatorium (Compositae)—A review. Plant Species Biol. 1, 99–116 (1986).CAS 
    Article 

    Google Scholar 
    16.Novak, S. J., Soltis, D. E. & Soltis, P. S. Ownbey’s Tragopogons: 40 years later. Am. J. Bot. 78, 1586–1600 (1991).Article 

    Google Scholar 
    17.Van Dijk, P. & Bakx-Schotman, T. Chloroplast DNA phylogeography and cytotype geography in autopolyploid Plantago media. Mol. Ecol. 6, 345–352 (1997).Article 

    Google Scholar 
    18.Martin, S. L. & Husband, B. C. Influence of phylogeny and ploidy on species ranges of North American angiosperms. J. Ecol. 97, 913–922 (2009).Article 

    Google Scholar 
    19.Suda, J., Kron, P., Husband, B. C. & Trávníček, P. Flow cytometry and ploidy: Applications in plant systematics, ecology and evolutionary biology. in Flow Cytometry with Plant Cells 103–130 (Wiley, 2007). https://doi.org/10.1002/9783527610921.ch5.20.Ramsey, J. & Ramsey, T. S. Ecological studies of polyploidy in the 100 years following its discovery. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369, 1–76 (2014).Article 

    Google Scholar 
    21.Goldblatt, P. Polyploidy in angiosperms: Monocotyledons. In Polyploidy. Basic Life Sciences Vol. 13 (ed. Lewis, W. H.) 219–239 (Springer, 1980).
    Google Scholar 
    22.Levy, A. A. & Feldman, M. The impact of polyploidy on grass genome evolution. Plant Physiol. 130, 1587–1593 (2002).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Kellogg, A. Flowering Plants Monocots Poaceae Vol. 13 (Springer, 2015).
    Google Scholar 
    24.Estep, M. C. et al. Allopolyploidy, diversification, and the Miocene grassland expansion. Proc. Natl. Acad. Sci. 111, 15149–15154 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Minaya, M. et al. Contrasting dispersal histories of broad- and fine-leaved temperate Loliinae grasses: Range expansion, founder events, and the roles of distance and barriers. J. Biogeogr. 44, 1980–1993 (2017).Article 

    Google Scholar 
    26.Torrecilla, P. & Catalán, P. Phylogeny of broad-leaved and fine-leaved Festuca lineages (Poaceae) based on nuclear ITS sequences. Syst. Bot. 27, 241–251 (2002).
    Google Scholar 
    27.Šmarda, P., Bureš, P., Horová, L., Foggi, B. & Rossi, G. Genome size and GC content evolution of Festuca: Ancestral expansion and subsequent reduction. Ann. Bot. 101, 421–433 (2008).28.Meusel, H., Jäger, E. & Weinert, E. Vergleichende Chorologie der Zentral-europäischen Flora (G. Fischer, 1965).
    Google Scholar 
    29.Kiedrzyński, M., Zielińska, K. M., Kiedrzyńska, E. & Jakubowska-Gabara, J. Regional climate and geology affecting habitat availability for a relict plant in a plain landscape: The case of Festuca amethystina L. in Poland. Plant Ecol. Divers. 8, 331–341 (2015).Article 

    Google Scholar 
    30.Kiedrzyński, M., Zielińska, K. M., Rewicz, A. & Kiedrzyńska, E. Habitat and spatial thinning improve the Maxent models performed with incomplete data. J. Geophys. Res. Biogeosci. 122, 1359–1370 (2017).Article 

    Google Scholar 
    31.Petrova, A. & Kozuharov, S. Citotaxonomicno proucvane na balgarski vidove ot roda Festuca L. in IV Nacionalna Konferencija Po Botanika 1 (ed. Trudova) 16–23 (1987).32.Stählin, A. Morphologische und zytologische Untersuchungen an Gramineen. Wiss. Arch. Landwirtschaft., Abt. A, Pflanzenbau 1, 330–398 (1929).33.Wittmann, H. & Strobl, W. Beitrag zur Kenntnis von Festuca amethystina L. im Bundesland Salzburg. Florist. Mitt. Salzburg 9, 3–8 (1984).34.La Sorte, F. A. & Jetz, W. Projected range contractions of montane biodiversity under global warming. Proc. R. Soc. B Biol. Sci. 277, 3401–3410 (2010).Article 

    Google Scholar 
    35.Elsen, P. R. & Tingley, M. W. Global mountain topography and the fate of montane species under climate change. Nat. Clim. Change 5, 772–776 (2015).ADS 
    Article 

    Google Scholar 
    36.Šmarda, P., Müller, J., Vraná, J. & Kočí, K. Ploidy level variability of some Central European fescues (Festuca subg. Festuca, Poaceae). Biologia 60, 1–6 (2005).
    Google Scholar 
    37.Rewicz, A. et al. Morphometric traits in the fine-leaved fescues depend on ploidy level: The case of Festuca amethystina L. PeerJ 2018, e5576 (2018).Article 

    Google Scholar 
    38.Roleček, J., Dřevojan, P. & Šmarda, P. First record of Festuca amethystina L. from the Transylvanian Basin (Romania). Contrib. Bot. 54, 91–97 (2019).Article 

    Google Scholar 
    39.Phillips, S. J. & Dudík, M. Modeling of species distribution with Maxent: New extensions and a comprehensive evaluation. Ecograpy 31, 161–175 (2008).Article 

    Google Scholar 
    40.Segraves, K. A., Thompson, J. N., Soltis, P. S. & Soltis, D. E. Multiple origins of polyploidy and the geographic structure of Heuchera grossulariifolia. Mol. Ecol. 8, 253–262 (1999).Article 

    Google Scholar 
    41.Levin, D. A. Minority cytotype exclusion in local plant populations. TAXON vol. 24. https://eurekamag.com/pdf/000/000139096.pdf (1975).42.Pils, G. Systematics, distribution, and karyology of the Festuca violacea Group (Poaceae) in the Eastern Alps. Plant Syst. Evol. 136, 73–124 (1980).Article 

    Google Scholar 
    43.Stebbins, G. L. Chromosomal Evolution in Higher Plants (Addison-Wesley, 1971).
    Google Scholar 
    44.Stutz, H. C. & Sanderson, S. C. Evolutionary studies in Atriplex: Chromosome races of A. confertifolia (shadscale). Am. J. Bot. 70, 1536–1547 (1983).Article 

    Google Scholar 
    45.Husband, B. C. & Schemske, D. W. Cytotype distribution at a diploid-tetraploid contact zone in Chamerion (Epilobium) angustifolium (Onagraceae). Am. J. Bot. 85, 1688–1694 (1998).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Hardy, O. J., Vanderhoeven, S., De Loose, M. & Meerts, P. Ecological, morphological and allozymic differentiation between diploid and tetraploid knapweeds (Centaurea jacea) from a contact zone in the Belgian Ardennes. New Phytol. 146, 281–290 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Gauthier, P., Lumaret, R. & Bédécarrats, A. Genetic variation and gene flow in Alpine diploid and tetraploid populations of Lotus (L. alpinus (DC) Schleicher/L. corniculatus L.). I. Insights from morphological and allozyme markers. Heredity (Edinb) 80, 683–693 (1998).CAS 
    Article 

    Google Scholar 
    48.Schönswetter, P. et al. Sympatric diploid and hexaploid cytotypes of Senecio carniolicus (Asteraceae) in the Eastern Alps are separated along an altitudinal gradient. J. Plant Res. 120, 721–725 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Petit, C., Bretagnolle, F. & Felber, F. Evolutionary consequences of diploid-polyploid hybrid zones in wild species. Trends Ecol. Evol. 14, 306–311 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Chumová, Z., Krejčíková, J., Mandáková, T., Suda, J. & Trávníček, P. Evolutionary and taxonomic implications of variation in nuclear genome size: Lesson from the grass genus Anthoxanthum (Poaceae). PLoS One 10, e0133748 (2015).Article 
    CAS 

    Google Scholar 
    51.Marchant, D. B., Soltis, D. E. & Soltis, P. S. Patterns of abiotic niche shifts in allopolyploids relative to their progenitors. New Phytol. 212, 708–718 (2016).Article 
    CAS 

    Google Scholar 
    52.Arrigo, N. et al. Is hybridization driving the evolution of climatic niche in Alyssum montanum. Am. J. Bot. 103, 1348–1357 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Laport, R. G., Minckley, R. L. & Ramsey, J. Ecological distributions, phenological isolation, and genetic structure in sympatric and parapatric populations of the Larrea tridentata polyploid complex. Am. J. Bot. 103, 1358–1374 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Mosquin, T. Evidence for autopolyploidy in Epilobium angustifolium (Onagraceae). Evolution (N. Y.) 21, 713–719 (1967).
    Google Scholar 
    55.Szafer, W. The mountain element in the flora of Polish Plain. Rozpr. Wydz. Mat. PAU Ser. 3 Dział B 69, 83–196 (1930).
    Google Scholar 
    56.Kiedrzyński, M., Zielińska, K. M., Kiedrzyńska, E. & Rewicz, A. Refugial debate: On small sites according to their function and capacity. Evol. Ecol. 31, 815–827 (2017).Article 

    Google Scholar 
    57.Babić, V. P. et al. Temperature and other microclimate conditions in the oak forests on Fruška Gora (Serbia). Therm. Sci. 19, S415–S425 (2015).Article 

    Google Scholar 
    58.Jakubowska-Gabara, J. Decline of Potentillo albae-Quercetum Libb. 1933 phytocoenoses in Poland. Vegetatio 124, 45–59 (1996).Article 

    Google Scholar 
    59.Roleček, J. Formalized classification of thermophilous oak forests in the Czech Republic: What brings the Cocktail method?. Preslia 79, 1–21 (2007).
    Google Scholar 
    60.Indreica, A. Festuca amethystina in the sessile oak forests from upper basin of Olt River. Contrib. Bot. 42, 11–18 (2007).
    Google Scholar 
    61.Jakubowska-Gabara, J. Festuca amethystina L. In The Polish Red Book of Plants. Pteridophytes and Vascular Plants (eds Kaźmierczakowa, R. et al.) 616–618 (Institute of Nature Conservation PAS, 2014).
    Google Scholar 
    62.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

    Google Scholar 
    63.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    64.Wei, T. & Simko, V. R package ‘corrplot’: Visualization of a Correlation Matrix (2017).65.Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using CANOCO 5 (Cambridge University Press, 2014). https://doi.org/10.1017/CBO9781139627061.Book 
    MATH 

    Google Scholar 
    66.Wilke, C. O. Ridgeline Plots in ‘ggplot2’. https://wilkelab.org/ggridges/index.html (2021).67.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

    Google Scholar 
    68.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: An open-source release of Maxent. Ecography (Cop.) 40, 887–893 (2017).Article 

    Google Scholar 
    69.Warren, D. L. & Seifert, S. Ecological niche modeling in Maxent: The importance of model complexity and the performance of model selection criteria. Ecol. Soc. Am. 21, 335–342 (2011).
    Google Scholar 
    70.Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).Article 

    Google Scholar 
    71.Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography (Cop.) 33, 607–611 (2010).
    Google Scholar 
    72.Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography (Cop.) 28, 385–393 (2005).Article 

    Google Scholar  More

  • in

    Contribution of conspecific negative density dependence to species diversity is increasing towards low environmental limitation in Japanese forests

    1.Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

    Google Scholar 
    2.Wright, J. S. Plant diversity in tropical forests: A review of mechanisms of species coexistence. Oecologia 130, 1–14 (2002).ADS 
    PubMed 
    Article 

    Google Scholar 
    3.Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).Article 

    Google Scholar 
    4.Connell, J. On the role of natural enemies in preventing competitive exclusion in some marine animals and rain forest trees. Dyn. Popul. 298, 312 (1971).
    Google Scholar 
    5.Terborgh, J. W. Toward a trophic theory of species diversity. PNAS 112, 11415–11422 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Johnson, D. J., Beaulieu, W. T., Bever, J. D. & Clay, K. Conspecific negative density dependence and forest diversity. Science 336, 904–907 (2012).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    7.LaManna, J. A. et al. Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392 (2017).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    8.Chisholm, R. A. & Muller-Landau, H. C. A theoretical model linking interspecific variation in density dependence to species abundances. Theor. Ecol. 4, 241–253 (2011).Article 

    Google Scholar 
    9.Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Chisholm, R. A. & Fung, T. Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science 360, eaar4685 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    11.Hülsmann, L. & Hartig, F. Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science 360, eaar2435 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    12.Detto, M., Visser, M. D., Wright, S. J. & Pacala, S. W. Bias in the detection of negative density dependence in plant communities. Ecol. Lett. 22, 1923–1939 (2019).PubMed 
    Article 

    Google Scholar 
    13.LaManna, J. A. et al. Response to Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science 360, eaar3824 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    14.LaManna, J. A. et al. Response to Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science 360, eaar5245 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    15.LaManna, J. A., Mangan, S. A. & Myers, J. A. Conspecific negative density dependence and why its study should not be abandoned. Ecosphere 12, e03322 (2021).Article 

    Google Scholar 
    16.Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Mittelbach, G. G. et al. Evolution and the latitudinal diversity gradient: Speciation, extinction and biogeography. Ecol. Lett. 10, 315–331 (2007).PubMed 
    Article 

    Google Scholar 
    18.Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).Article 

    Google Scholar 
    19.Ricklefs, R. E. & He, F. Region effects influence local tree species diversity. PNAS 113, 674–679 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Comita, L. S. et al. Testing predictions of the Janzen-Connell hypothesis: A meta-analysis of experimental evidence for distance- and density-dependent seed and seedling survival. J. Ecol. 102, 845–856 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Currie, D. J. Energy and large-scale patterns of animal- and plant-species richness. Am. Nat. 137, 27–49 (1991).Article 

    Google Scholar 
    22.Grosso, S. D. et al. Global potential net primary production predicted from vegetation class, precipitation, and temperature. Ecology 89, 2117–2126 (2008).PubMed 
    Article 

    Google Scholar 
    23.Chase, J. M. Stochastic Community Assembly Causes Higher Biodiversity in More Productive Environments. Science 27, (2010).24.O’Brien, E. M. Climatic gradients in woody plant species richness: Towards an explanation based on an analysis of Southern Africa’s woody flora. J. Biogeography 20, 181–198 (1993).Article 

    Google Scholar 
    25.McCain, C. M. & Grytnes, J.-A. Elevational Gradients in Species Richness. In eLS (American Cancer Society, 2010).26.Barry, R. G. Mountain Weather and Climate (Cambridge University Press, 2008).Book 

    Google Scholar 
    27.LaManna, J. A., Walton, M. L., Turner, B. L. & Myers, J. A. Negative density dependence is stronger in resource-rich environments and diversifies communities when stronger for common but not rare species. Ecol. Lett. 19, 657–667 (2016).PubMed 
    Article 

    Google Scholar 
    28.Zhu, K., Woodall, C. W., Monteiro, J. V. D. & Clark, J. S. Prevalence and strength of density-dependent tree recruitment. Ecology 96, 2319–2327 (2015).PubMed 
    Article 

    Google Scholar 
    29.Yao, J. et al. Abiotic niche partitioning and negative density dependence across multiple life stages in a temperate forest in northeastern China. J. Ecol. 108, 1299–1310 (2020).Article 

    Google Scholar 
    30.Leigh, E. G. et al. Why do some tropical forests have so many species of trees?. Biotropica 36, 447–473 (2004).
    Google Scholar 
    31.Terborgh, J. Enemies maintain hyperdiverse tropical forests. Am. Nat. 179, 303–314 (2012).PubMed 
    Article 

    Google Scholar 
    32.Altman, J. et al. Linking spatiotemporal disturbance history with tree regeneration and diversity in an old-growth forest in northern Japan. PPEES 21, 1–13 (2016).
    Google Scholar 
    33.Kubota, Y., Hirao, T., Fujii, S., Shiono, T. & Kusumoto, B. Beta diversity of woody plants in the Japanese archipelago: The roles of geohistorical and ecological processes. J. Biogeogr. 41, 1267–1276 (2014).Article 

    Google Scholar 
    34.Mori, A. S. Local and biogeographic determinants and stochasticity of tree population demography. J. Ecol. 107, 1276–1287 (2019).Article 

    Google Scholar 
    35.Oohata, S. Distribution of tree species along the temperature gradient in the Japan archipelago (ii).: Life form and species distribution. Jap. J. Ecol. 40, 71–84 (1990).ADS 

    Google Scholar 
    36.Kira, T. A Climatological Interpretation of Japanese Vegetation Zones 21–30 (Springer, 1977).
    Google Scholar 
    37.Mori, A. S. Environmental controls on the causes and functional consequences of tree species diversity. J. Ecol. 106, 113–125 (2018).Article 

    Google Scholar 
    38.Suzuki, S. N., Ishihara, M. I. & Hidaka, A. Regional-scale directional changes in abundance of tree species along a temperature gradient in Japan. Glob. Chan. Biol. 21, 3436–3444 (2015).ADS 
    Article 

    Google Scholar 
    39.Hara, M. Analysis of seedling banks of a climax beech forest: Ecological importance of seedling sprouts. Vegetatio 71, 67–74 (1987).
    Google Scholar 
    40.Homma, K. Effects of snow pressure on growth form and life history of tree species in Japanese beech forest. J. Veg. Sci. 8, 781–788 (1997).Article 

    Google Scholar 
    41.Gansert, D. Treelines of the Japanese Alps—altitudinal distribution and species composition under contrasting winter climates. Flora 199, 143–156 (2004).Article 

    Google Scholar 
    42.Hukusima, T. et al. New phytosociological classification of beech forests in Japan. Jpn. J. Ecol. 45, 79–98 (1995).
    Google Scholar 
    43.Matsui, T. et al. Probability distributions, vulnerability and sensitivity in Fagus crenata forests following predicted climate changes in Japan. J. Veg. Sci. 15, 605–614 (2004).Article 

    Google Scholar 
    44.Johnson, D. J., Condit, R., Hubbell, S. P. & Comita, L. S. Abiotic niche partitioning and negative density dependence drive tree seedling survival in a tropical forest. Proc. R. Soc. B 284, 20172210 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Ishihara, M. I. et al. Forest stand structure, composition, and dynamics in 34 sites over Japan. Ecol. Res. 26, 1007–1008 (2011).Article 

    Google Scholar 
    46.Law, R. et al. Ecological information from spatial patterns of plants: Insights from point process theory. J. Ecol. 97, 616–628 (2009).Article 

    Google Scholar 
    47.Wright, S. J. et al. Reproductive size thresholds in tropical trees: Variation among individuals, species and forests. J. Trop. Ecol. 21, 307–315 (2005).Article 

    Google Scholar 
    48.Zhu, Y., Comita, L. S., Hubbell, S. P. & Ma, K. Conspecific and phylogenetic density-dependent survival differs across life stages in a tropical forest. J. Ecol. 103, 957–966 (2015).Article 

    Google Scholar 
    49.Ripley, B. D. Spatial point pattern analysis in ecology. In Develoments in Numerical Ecology (eds Legendre, P. & Legendre, L.) 407–429 (Springer, 1987).Chapter 

    Google Scholar 
    50.Wiegand, T. & Moloney, K. A. Handbook of Spatial Point-Pattern Analysis in Ecology (CRC Press, 2013).Book 

    Google Scholar 
    51.Loosmore, N. B. & Ford, E. D. Statistical inference using the G or K point pattern spatial statistics. Ecology 87, 1925–1931 (2006).PubMed 
    Article 

    Google Scholar 
    52.R Core Team. R: A Language and Environment for Statistical Computing (2020).53.Baddeley, A. & Turner, R. spatstat: An R Package for Analyzing Spatial Point Patterns. J. Stat. Soft. 12, 1–42 (2005).Article 

    Google Scholar 
    54.Wills, C., Condit, R., Foster, R. B. & Hubbell, S. P. Strong density- and diversity-related effects help to maintain tree species diversity in a neotropical forest. PNAS 94, 1252–1257 (1997).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Givnish, T. J. On the causes of gradients in tropical tree diversity. J. Ecol. 87, 193–210 (1999).Article 

    Google Scholar 
    56.Fibich, P., Vítová, A. & Lepš, J. Interaction between habitat limitation and dispersal limitation is modulated by species life history and external conditions: A stochastic matrix model approach. Comm. Ecol. 19, 9–20 (2018).Article 

    Google Scholar 
    57.Miyawaki, A. A vegetation ecological view of the Japanese archipelago. Bull. Inst. Environ. Sci. Technol. Yokohama Natl. Univ. 11, 85–101 (1984).
    Google Scholar 
    58.Mori, A. S. et al. Community assembly processes shape an altitudinal gradient of forest biodiversity. Glo. Ecol. Biogeogr. 22, 878–888 (2013).Article 

    Google Scholar 
    59.Grime, J. P. Plant Strategies, Vegetation Processes, and Ecosystem Properties (Wiley, 2001).
    Google Scholar 
    60.Brown, C., Law, R., Illian, J. B. & Burslem, D. F. R. P. Linking ecological processes with spatial and non-spatial patterns in plant communities. J. Ecol. 99, 1402–1414 (2011).Article 

    Google Scholar 
    61.Bastias, C. C. et al. Species richness influences the spatial distribution of trees in European forests. Oikos 129, 380–390 (2020).Article 

    Google Scholar 
    62.Hülsmann, L., Chisholm, R. A. & Hartig, F. Is variation in conspecific negative density dependence driving tree diversity patterns at large scales?. Trends Ecol. Evol. 36, 151–163 (2021).PubMed 
    Article 

    Google Scholar 
    63.Damgaard, C. & Weiner, J. It’s about time: A critique of macroecological inferences concerning plant competition. Trends Ecol. Evol. 32, 86–87 (2017).PubMed 
    Article 

    Google Scholar 
    64.Murata, I. et al. Effects of sika deer (Cervus nippon) and dwarf bamboo (Sasamorpha borealis) on seedling emergence and survival in cool-temperate mixed forests in the Kyushu Mountains. J. For. Res. 14, 296–301 (2009).Article 

    Google Scholar 
    65.Ackerly, D. D. et al. The geography of climate change: Implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).Article 

    Google Scholar  More

  • in

    Presence and biodistribution of perfluorooctanoic acid (PFOA) in Paracentrotus lividus highlight its potential application for environmental biomonitoring

    Samples collectionThree sampling campaigns were carried out at the two sample sites (A and B) on the coast of north-western Sicily (Fig. 1a) chosen for this study. The main features of the sites and sampling details are summarized in Table S1 (Supplementary Information). A total of 90 specimens of sea urchins Paracentrotus lividus (45 specimen per each site), 30 l of seawater (15 per site), 40 samples (20 per site) of sea grass Posidonia oceanica (less than 5 cm leaf fragments, according to the institutional and national ethical guidelines) were collected and analyzed together with 30 l of brackish water from site B (15 l from each creek).Figure 1Map of the sampling site. (a) Geographic area, (b) bathymetric chart and (c) relative distance between sample sites; (d, e) close ups of sampling sites. (Images obtained by courtesy of Google Earth Pro and map.openseamap.org).Full size imageThe samplings activity was authorized by the Capitaneria di Porto of Palermo with protocol number: 0029430. In the absence of data about PFOA contamination in the most recent report about chemical contamination in the coastal region subjected to this study31, the choice of sample sites was based on supposedly different status of pollution based on the site position or proximity to human activities (e.g. restaurants, pipeline, sewages, etc.).Site A (see Fig. 1d), was chosen assuming a lower state of pollution based on its position in proximity to Capo Zafferano, at the northern extremity of S. Elia’s bay, with an average depth of 11 m and rocky seabed (see Fig. 1b and Supplementary Information: Table S1). Conversely, Site B (see Fig. 1e was chosen in the same coastal area (only 4.7 km away from Site A) assuming a higher state of pollution due to its position located on the southern side of Solanto promontory, nearby a pipeline and the mouths of two small creeks from inland, with a shallow (3 m) sandy seabed and where a bathing prohibition order is in place32 (see Fig. 1b, c and Supplementary Information: Table S1).The biodistribution of PFOA in the various matrices was evaluated by analyzing sea urchin’s coelomocytes (CC) (90 samples) and coelomic fluid (CF) (90 samples), as well as gonads (G) (63 samples from 32 sea urchins collected in site A and 31 sea urchins collected in site B), or mixed organs (MIX) (27 samples from 13 sea urchins collected in site A and 14 sea urchins collected in site B) consisting of a homogenized mixture of urchin’s inner matrices when gonads were not developed enough for sampling. Due to their mutually exclusive nature the latter two datasets (G and MIX) were merged and labelled as “Gonads or Mixed organs” (GoM) for statistical analysis and graphical representations that needed a uniform dataset of 45 items per site. Further details on the collection of matrices and their labelling are described in the Supplementary Information.The size of the sea urchins (horizontal diameter without spines) ranged between 30 and 51 mm indicating specimen that have lived in their respective site approximately from 3 to 5 years25.PFOA extraction and analysisMaterials, equipment and software are described in the Supplementary Information.PFOA extraction procedures were adapted33 to the type of matrix to be analyzed. Recovery percentages (R %) were checked per each batch of analyses by spiking blank samples with different amounts of PFOA analytical standard before the extraction procedure33.Spiked samples underwent the same extraction procedure of unspiked samples and the percentage of recovery R was calculated according to Eq. 1, where Cspike is the known concentration of spiked PFOA, Dspiked is the instrumental (LC–MS) analytical response of the spiked sample (i.e. the “detected” concentration), Dunspiked is the analytical response of the unspiked sample. R was then used in Eq. 2 to calculate the actual values, [PFOA], of PFOA concentrations in unspiked analyzed samples.$$ {text{R}} = 100 times left( {{text{D}}_{{{text{spiked}}}} – {text{D}}_{{{text{unspiked}}}} } right)/{text{C}}_{{{text{spike}}}} $$
    (1)
    $$ left[ {{text{PFOA}}} right] = 100 times {text{D}}_{{{text{unspiked}}}} /{text{R}} $$
    (2)
    With the exception of [PFOA]seawater and [PFOA]creek, which are expressed as nanograms per liter (ppt), all other PFOA concentrations are expressed in nanograms per gram of matrix (ppb).The PFOA standard was used for calibration before each batch of analyses and a linear response (R2  > 0.99) was recorded in the concentration range from 0.1 to 1000 ppb. The RSDs on three replicates were below 10%. LOD (0.1 ppb) and LOQ (1.0 ppb) were quantified by IUPAC method. LC–MS analyses were performed in the negative ion-monitoring mode (see Supplementary Information).For the analysis of P. lividus specimens, an estimate of the total PFOA concentration, [PFOA]TOT in ng/g, in each sea urchin has been calculated considering the sampled weight (W) in grams of each matrix (Eq. 3):$$ left[ {{text{PFOA}}} right]_{{{text{TOT}}}} = left( {{text{W}}_{{{text{CF}}}} left[ {{text{PFOA}}} right]_{{{text{CF}}}} + {text{W}}_{{{text{CC}}}} left[ {{text{PFOA}}} right]_{{{text{CC}}}} + {text{W}}_{{{text{GoM}}}} left[ {{text{PFOA}}} right]_{{{text{GoM}}}} } right)/left( {{text{W}}_{{{text{CF}}}} + {text{W}}_{{{text{CC}}}} + {text{W}}_{{{text{GoM}}}} } right) $$
    (3)
    Water analysisDuring each one of the 3 sampling campaigns, 2 samples of seawater (5 l from Site A and 5 l from site B) and 2 samples of brackish water (5 l from each creek mouths in site B) were collected for a total of 6 seawater samples and 6 brackish water samples.Samples were checked for the presence of PFOA by solid phase extraction (SPE) (see Supplementary Information) followed by LC–MS analysis34.The percentage of recovery, calculated according to Eq. 1, was R = 120%. [PFOA]seawater and [PFOA]creek concentrations (ng/L) were determined from analytical data according to Eq. 2.
    Posidonia oceanica analysisA total of 40 samples of leaves were collected from different individuals of P. oceanica (20 samples from site A and 20 samples from site B). Each sample was cut in tiny pieces and homogenized using an agate mortar and pestle, weighed (0.5 g) and transferred to a glass tube for extraction (see Supplementary Information).The percentage of recovery, calculated according to Eq. 1, was R = 70%. [PFOA]P. oceanica concentrations (ng/g) were determined from analytical data according to Eq. 2.Coelomocytes and coelomic fluid analysisThe coelomic fluid, containing also the coelomocyte population, was taken from all the ninety collected specimens (45 per site) by inserting an ultrathin and sharp needle (32G 0.26 mm × 12 mm) of a 1 mL syringe through the peristomal membrane35. All samples were centrifuged at 4 °C and 1500 rpm for 5 min in a 5804R refrigerated centrifuge (Eppendorf, Germany) thus separating the supernatant coelomic fluid (CF) from the coelomocytes (CC). CF and CC were then weighed and placed in different glass tubes for subsequent PFOA extractions (see Supplementary Information).The percentage of recovery, calculated according to Eq. 1, was R = 28% for CF and R = 68% for CC. [PFOA]CF and [PFOA]CC concentrations (ng/g) were determined from analytical data according to Eq. 2.Gonads analysisThe extraction of PFOA from 63 samples of gonads (32 from Site A and 31 from Site B) was performed with LC–MS grade methanol following the same procedure used for extraction from CF and CC (5 mL for samples greater than 0.5 g samples; 2.5 mL for samples between 0.1 g and 0.5 g). In case of undetected PFOA (considered as zero-values in graphics and statistical data treatment), analyses were repeated for confirmation on concentrated sample extracts.Twenty spiked samples were prepared from the most abundant samples of gonads (10 spiked samples per site), by adding 25 µL of an aqueous 1 mg/L stock solution of PFOA to 0.25 g of gonads samples. The percentage of PFOA recovery from gonads, calculated according to Eq. 1, was R = 73%. [PFOA]G concentrations (ng/g) were determined from analytical data according to Eq. 2.Mixed organs analysisIn 27 specimens of sea urchins (13 from Site A and 14 from Site B), the developmental status was not sufficient to collect at least 0.1 g of gonad sample. For these individuals, organs remaining after CF and CC collection, mainly intestine and undeveloped gonads, were mixed together and extracted similarly to the other matrices.Spiked samples were prepared by adding 25 µL of an aqueous 1 mg/L stock solution of PFOA to 0.25 g of mixed organs (MIX) samples. The percentage of PFOA recovery from MIX, calculated according to Eq. 1, was R = 20%. [PFOA]MIX concentrations (ng/g) were determined from analytical data according to Eq. 2.Statistical analyses and graphical data representationThe distribution of PFOA concentrations in all the sampled matrices from collected sea urchins is graphically represented by box and jitter plot (Fig. 2) where the 25–75 percentiles are drawn using a box; minimum and maximum are shown at the end of the thin lines (whiskers), while the median is marked as a horizontal line in the boxfitting. Statistical tests and linear fittings were used to evaluate data significance and correlations (see Supplementary Information).Figure 2Box and jitter plot showing the concentrations of PFOA found in the Coelomic Fluid (CF) Coelomocytes (CC) and Gonads or Mixed organs (GoM), as well as the total PFOA concentration (TOT), in 45 specimens of P. lividus collected from Site A (left side) and in 45 specimens of P. lividus collected from Site B (right side).Full size imageA permutational multivariate analysis of variance PERMANOVA36 was performed to evaluate the differences in the PFOA concentrations between the two groups of sea urchins collected from site A and site B. The experimental design comprised of one factor (Site) two levels (fixed and orthogonal) and four variables corresponding to the concentrations of PFOA in each type of sample analysed (coelomocytes, coelomic fluid, gonad or mixed organs) including the estimated total PFOA concentration. Each term in the analysis was tested by 999 random permutations.Finally, Principal Component Analysis (PCA) (see Supplementary Information: PCA tables and graphs) was performed on a dataset, containing five variables. Specifically sea urchin’s size and PFOA concentrations in each type of sample (CF, CC, and GoM) as well as in the entire sea urchin (TOT), to verify the multivariate nature of data in a relatively small number of dimensions, thus limiting the loss of information. More

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    Electric field detection as floral cue in hoverfly pollination

    1.Chittka, L. & Raine, N. E. Recognition of flowers by pollinators. Curr. Opin. Plant Biol. 9, 428–435 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Raguso, R. A. Flowers as sensory billboards: Progress towards an integrated understanding of floral advertisement. Curr. Opin. Plant Biol. 7, 434–440 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    3.Goulson, D., Stout, J. C. & Hawson, S. A. Can flower constancy in nectaring butterflies be explained by Darwin’s interference hypothesis?. Oecologia 112, 225–231 (1997).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Goulson, D. & Wright, N. P. Flower constancy in the hoverflies Episyrphus balteatus (Degeer) and Syrphus ribesii (L.) (Syrphidae). Behav. Ecol. 9, 213–219 (1998).Article 

    Google Scholar 
    5.Von Arx, M., Goyret, J., Davidowitz, G. & Raguso, R. A. Floral humidity as a reliable sensory cue for profitability assessment by nectar-foraging hawkmoths. Proc. Natl. Acad. Sci. 109, 9471–9476 (2012).ADS 
    Article 

    Google Scholar 
    6.Leonard, A. S., Dornhaus, A. & Papaj, D. R. Forget-me-not: Complex floral displays, inter-signal interactions, and pollinator cognition. Curr. Zool. 57, 215–224 (2011).Article 

    Google Scholar 
    7.Vaknin, Y., Gan-Mor, S., Bechar, A., Ronen, B. & Eisikowitch, D. The role of electrostatic forces in pollination. Plant Syst. Evol. 222, 133–142. https://doi.org/10.1007/bf00984099 (2000).Article 

    Google Scholar 
    8.Bowker, G. E. & Crenshaw, H. C. Electrostatic forces in wind-pollination—Part 2: Simulations of pollen capture. Atmos. Environ. 41, 1596–1603 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    9.Erickson, E. Surface electric potentials on worker honeybees leaving and entering the hive. J. Apic. Res. 14, 141–147 (1975).Article 

    Google Scholar 
    10.Edwards, D. Electrostatic charges on insects due to contact with different substrates. Can. J. Zool. 40, 579–584 (1962).Article 

    Google Scholar 
    11.Vaknin, Y., Gan-Mor, S., Bechar, A., Ronen, B. & Eisikowitch, D. Pollen and Pollination 133–142 (Springer, 2000).Book 

    Google Scholar 
    12.Eskov, E. & Sapozhnikov, A. Mechanism of generation and perception of electric fields by honey bees. Biofizika 21, 1097–1102 (1976).CAS 

    Google Scholar 
    13.Clarke, D., Whitney, H., Sutton, G. & Robert, D. Detection and learning of floral electric fields by bumblebees. Science 340, 66–69 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Bowker, G. E. & Crenshaw, H. C. Electrostatic forces in wind-pollination—Part 1: Measurement of the electrostatic charge on pollen. Atmos. Environ. 41, 1587–1595 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    15.Gan-Mor, S., Schwartz, Y., Bechar, A., Eisikowitch, D. & Manor, G. Relevance of electrostatic forces in natural and artificial pollination. Can. Agric. Eng. 37, 189–194 (1995).
    Google Scholar 
    16.Colin, M., Richard, D. & Chauzy, S. Measurement of electric charges carried by bees: Evidence of biological variations. J. Bioelectr. 10, 17–32 (1991).Article 

    Google Scholar 
    17.Pinillos, V. & Cuevas, J. Artificial pollination in tree crop production. Horticult. Rev. 2, 2 (2008).
    Google Scholar 
    18.Corbet, S. A., Beament, J. & Eisikowitch, D. Are electrostatic forces involved in pollen transfer?. Plant Cell Environ. 5, 125–129 (1982).Article 

    Google Scholar 
    19.Inouye, D. W., Larson, B. M., Ssymank, A. & Kevan, P. G. Flies and flowers III: Ecology of foraging and pollination. J. Pollin. Ecol. 16, 115–133 (2015).Article 

    Google Scholar 
    20.Kanstrup, J. & Olesen, J. M. Plant-flower visitor interactions in a neotropical rain forest canopy: Community structure and generalisation level. The Scand. Assoc. Pollin. Ecol. honours knut Fægri 2, 33–42 (2000).
    Google Scholar 
    21.Orford, K. A., Vaughan, I. P. & Memmott, J. The forgotten flies: The importance of non-syrphid Diptera as pollinators. Proc. R. Soc. B Biol. Sci. 282, 20142934 (2015).Article 

    Google Scholar 
    22.Sakurai, A. & Takahashi, K. Flowering phenology and reproduction of the Solidago virgaurea L. complex along an elevational gradient on M t N orikura, central Japan. Plant Sp. Biol. 32, 270–278 (2017).Article 

    Google Scholar 
    23.Forup, M. L., Henson, K. S., Craze, P. G. & Memmott, J. The restoration of ecological interactions: Plant–pollinator networks on ancient and restored heathlands. J. Appl. Ecol. 45, 742–752 (2008).Article 

    Google Scholar 
    24.Solomon, M. & Kendall, D. Pollination by the syrphid fly, Eristalis tenax. (1970).25.Kendall, D., Wilson, D., Guttridge, C. & Anderson, H. Testing Eristalis as a pollinator of covered crops. Long Ashton Res. Stn. Rep. 1971, 120–121 (1971).
    Google Scholar 
    26.Ohsawa, R. & Namai, H. The effect of insect pollinators on pollination and seed setting in Brassica campestris cv. Nozawana and Brassica juncea cv Kikarashina. Jpn. J. Breed. 37, 453–463 (1987).ADS 
    Article 

    Google Scholar 
    27.Jauker, F. & Wolters, V. Hover flies are efficient pollinators of oilseed rape. Oecologia 156, 819–823 (2008).ADS 
    PubMed 
    Article 

    Google Scholar 
    28.Rader, R. et al. Alternative pollinator taxa are equally efficient but not as effective as the honeybee in a mass flowering crop. J. Appl. Ecol. 46, 1080–1087 (2009).Article 

    Google Scholar 
    29.Kalmijn, A. J. The electric sense of sharks and rays. J. Exp. Biol. 55, 371–383 (1971).CAS 
    PubMed 
    Article 

    Google Scholar 
    30.Clarke, D., Morley, E. & Robert, D. The bee, the flower, and the electric field: Electric ecology and aerial electroreception. J. Comp. Physiol. A. 203, 737–748 (2017).Article 

    Google Scholar 
    31.Greggers, U. et al. Reception and learning of electric fields in bees. Proc. R. Soc. B Biol. Sci. 280, 20130528 (2013).Article 

    Google Scholar 
    32.Casas, J. & Dangles, O. Physical ecology of fluid flow sensing in arthropods. Annu. Rev. Entomol. 55, 505–520 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Tautz, J. & Rostás, M. Honeybee buzz attenuates plant damage by caterpillars. Curr. Biol. 18, R1125–R1126 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Bathellier, B., Steinmann, T., Barth, F. G. & Casas, J. Air motion sensing hairs of arthropods detect high frequencies at near-maximal mechanical efficiency. J. R. Soc. Interface 9, 1131–1143 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Newland, P. L. et al. Static electric field detection and behavioural avoidance in cockroaches. J. Exp. Biol. 211, 3682–3690 (2008).PubMed 
    Article 

    Google Scholar 
    36.Sutton, G. P., Clarke, D., Morley, E. L. & Robert, D. Mechanosensory hairs in bumblebees (Bombus terrestris) detect weak electric fields. Proc. Natl. Acad. Sci. 113, 7261–7265 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Wędzony, M. & Filek, M. Changes of electric potential in pistils of Petunia hybrida Hort. and Brassica napus L. during pollination. Acta Physiol. Plantarum 20, 291–297 (1998).Article 

    Google Scholar 
    38.Stout, J. C. & Goulson, D. The use of conspecific and interspecific scent marks by foraging bumblebees and honeybees. Anim. Behav. 62, 183–189 (2001).Article 

    Google Scholar 
    39.Weiss, M. R. Floral color change: A widespread functional convergence. Am. J. Bot. 82, 167–185 (1995).Article 

    Google Scholar 
    40.Waser, N. M. & Price, M. V. Pollinator behaviour and natural selection for flower colour in Delphinium nelsonii. Nature 302, 422 (1983).ADS 
    Article 

    Google Scholar 
    41.Shimozawa, T., Murakami, J. & Kumagai, T. Sensors and Sensing in Biology and Engineering 145–157 (Springer, 2003).Book 

    Google Scholar 
    42.Khan, S. & Hanif, H. Diversity and fauna of hoverflies (Syrphidae) in Chakwal, Pakistan. Int. J of Zool. Stud. 1, 22–23 (2016).
    Google Scholar 
    43.Khan, S. A. & Hanif, H. First record and redescription of Cheilosia albipila syrphid flies from Punjab, Pakistan. Int. J. Zool. Res. 1, 2 (2016).
    Google Scholar 
    44.Shehzad, A. et al. Faunistic study of hover flies (Diptera: Syrphidae) of Pakistan. Orient. Insects 51, 197–220 (2017).Article 

    Google Scholar 
    45.Nicholas, S., Thyselius, M., Holden, M. & Nordström, K. Rearing and long-term maintenance of eristalis tenax hoverflies for research studies. JoVE https://doi.org/10.3791/57711 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Gilbert, F. S. Foraging ecology of hoverflies: Morphology of the mouthparts in relation to feeding on nectar and pollen in some urban species. Ecol. Entomol. 2, 2 (1981).
    Google Scholar 
    47.Nicholas, S., Thyselius, M., Holden, M. & Nordström, K. Rearing and long-term maintenance of Eristalis tenax hoverflies for research studies. J. Vis. Exp. JoVE 2, 2 (2018).
    Google Scholar 
    48.Hogg, B. N., Bugg, R. L. & Daane, K. M. Attractiveness of common insectary and harvestable floral resources to beneficial insects. Biol. Control 56, 76–84 (2011).Article 

    Google Scholar 
    49.McGonigle, D. F., Jackson, C. W. & Davidson, J. L. Triboelectrification of houseflies (Musca domestica L.) walking on synthetic dielectric surfaces. J. Electrostat. 54, 167–177 (2002).Article 

    Google Scholar 
    50.Koh, K., Montgomery, C., Clarke, D., Morley, E. & Robert, D. in Journal of Physics: Conference Series. 012001 (IOP Publishing).51.Rycroft, M., Israelsson, S. & Price, C. The global atmospheric electric circuit, solar activity and climate change. J. Atmos. Solar Terr. Phys. 62, 1563–1576 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    52.Whitney, H. M., Dyer, A., Chittka, L., Rands, S. A. & Glover, B. J. The interaction of temperature and sucrose concentration on foraging preferences in bumblebees. Naturwissenschaften 95, 845–850 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Stanković, B. & Davies, E. Both action potentials and variation potentials induce proteinase inhibitor gene expression in tomato. FEBS Lett. 390, 275–279 (1996).PubMed 
    Article 

    Google Scholar  More

  • in

    The macronuclear genome of the Antarctic psychrophilic marine ciliate Euplotes focardii reveals new insights on molecular cold adaptation

    1.Pucciarelli, S. et al. Molecular cold-adaptation of protein function and gene regulation: the case for comparative genomic analyses in marine ciliated protozoa. Mar Genomics 2, 57–66. https://doi.org/10.1016/j.margen.2009.03.008 (2009).Article 
    PubMed 

    Google Scholar 
    2.Pucciarelli, S., Marziale, F., Di Giuseppe, G., Barchetta, S. & Miceli, C. Ribosomal cold-adaptation: characterization of the genes encoding the acidic ribosomal P0 and P2 proteins from the Antarctic ciliate Euplotes focardii. Gene 360, 103–110. https://doi.org/10.1016/j.gene.2005.06.007 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    3.Pucciarelli, S. & Miceli, C. Characterization of the cold-adapted alpha-tubulin from the psychrophilic ciliate Euplotes focardii. Extremophiles 6, 385–389. https://doi.org/10.1007/s00792-002-0268-5 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    4.Yang, G. et al. Characterization and comparative analysis of psychrophilic and mesophilic alpha-amylases from Euplotes species: a contribution to the understanding of enzyme thermal adaptation. Biochem Biophys Res Commun 438, 715–720. https://doi.org/10.1016/j.bbrc.2013.07.113 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    5.Prescott, D. M. The DNA of ciliated protozoa. Microbiol Rev 58, 233–267 (1994).CAS 
    Article 

    Google Scholar 
    6.Mollenbeck, M. & Klobutcher, L. A. De novo telomere addition to spacer sequences prior to their developmental degradation in Euplotes crassus. Nucleic Acids Res 30, 523–531 (2002).Article 

    Google Scholar 
    7.Swart, E. C. et al. The Oxytricha trifallax macronuclear genome: a complex eukaryotic genome with 16,000 tiny chromosomes. PLoS Biol 11, e1001473. https://doi.org/10.1371/journal.pbio.1001473 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Heyse, G., Jonsson, F., Chang, W. J. & Lipps, H. J. RNA-dependent control of gene amplification. Proc Natl Acad Sci U S A 107, 22134–22139. https://doi.org/10.1073/pnas.1009284107 (2010).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Nowacki, M., Haye, J. E., Fang, W., Vijayan, V. & Landweber, L. F. RNA-mediated epigenetic regulation of DNA copy number. Proc Natl Acad Sci U S A 107, 22140–22144. https://doi.org/10.1073/pnas.1012236107 (2010).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Dayeh, V. R. et al. Comparing a ciliate and a fish cell line for their sensitivity to several classes of toxicants by the novel application of multiwell filter plates to Tetrahymena. Res Microbiol 156, 93–103. https://doi.org/10.1016/j.resmic.2004.08.005 (2005).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    11.Detrich, H. W., 3rd, Parker, S. K., Williams, R. C., Jr., Nogales, E. & Downing, K. H. Cold adaptation of microtubule assembly and dynamics. Structural interpretation of primary sequence changes present in the alpha- and beta-tubulins of Antarctic fishes. J Biol Chem 275, 37038–37047. https://doi.org/10.1074/jbc.M005699200 (2000).12.Manka, S. W. & Moores, C. A. Microtubule structure by cryo-EM: snapshots of dynamic instability. Essays Biochem 62, 737–751. https://doi.org/10.1042/EBC20180031 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Inclan, Y. F. & Nogales, E. Structural models for the self-assembly and microtubule interactions of gamma-, delta- and epsilon-tubulin. J Cell Sci 114, 413–422 (2001).CAS 
    Article 

    Google Scholar 
    14.Chiappori, F. et al. Structural thermal adaptation of beta-tubulins from the Antarctic psychrophilic protozoan Euplotes focardii. Proteins 80, 1154–1166. https://doi.org/10.1002/prot.24016 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Marziale, F. et al. Different roles of two gamma-tubulin isotypes in the cytoskeleton of the Antarctic ciliate Euplotes focardii: remodelling of interaction surfaces may enhance microtubule nucleation at low temperature. FEBS J 275, 5367–5382. https://doi.org/10.1111/j.1742-4658.2008.06666.x (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    16.Pucciarelli, S., Miceli, C. & Melki, R. Heterologous expression and folding analysis of a beta-tubulin isotype from the Antarctic ciliate Euplotes focardii. Eur J Biochem 269, 6271–6277 (2002).CAS 
    Article 

    Google Scholar 
    17.Gromer, S., Urig, S. & Becker, K. The thioredoxin system–from science to clinic. Med Res Rev 24, 40–89. https://doi.org/10.1002/med.10051 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    18.Birben, E., Sahiner, U. M., Sackesen, C., Erzurum, S. & Kalayci, O. Oxidative stress and antioxidant defense. World Allergy Organ J 5, 9–19. https://doi.org/10.1097/WOX.0b013e3182439613 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Alin, P., Danielson, U. H. & Mannervik, B. 4-Hydroxyalk-2-enals are substrates for glutathione transferase. FEBS Lett 179, 267–270 (1985).CAS 
    Article 

    Google Scholar 
    20.Juganson, K. et al. Mechanisms of toxic action of silver nanoparticles in the protozoan Tetrahymena thermophila: From gene expression to phenotypic events. Environ Pollut 225, 481–489. https://doi.org/10.1016/j.envpol.2017.03.013 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    21.Clark, M. S., Fraser, K. P. & Peck, L. S. Antarctic marine molluscs do have an HSP70 heat shock response. Cell Stress Chaperones 13, 39–49. https://doi.org/10.1007/s12192-008-0014-8 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Tomanek, L. The heat-shock response: its variation, regulation and ecological importance in intertidal gastropods (genus Tegula). Integr Comp Biol 42, 797–807. https://doi.org/10.1093/icb/42.4.797 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    23.Morimoto, R. I., Kline, M. P., Bimston, D. N. & Cotto, J. J. The heat-shock response: regulation and function of heat-shock proteins and molecular chaperones. Essays Biochem 32, 17–29 (1997).CAS 
    PubMed 

    Google Scholar 
    24.Gonzalez-Aravena, M. et al. HSP70 from the Antarctic sea urchin Sterechinus neumayeri: molecular characterization and expression in response to heat stress. Biol Res 51, 8. https://doi.org/10.1186/s40659-018-0156-9 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Hofmann, G. E., Buckley, B. A., Airaksinen, S., Keen, J. E. & Somero, G. N. Heat-shock protein expression is absent in the antarctic fish Trematomus bernacchii (family Nototheniidae). J Exp Biol 203, 2331–2339 (2000).CAS 
    Article 

    Google Scholar 
    26.La Terza, A., Papa, G., Miceli, C. & Luporini, P. Divergence between two Antarctic species of the ciliate Euplotes, E. focardii and E. nobilii, in the expression of heat-shock protein 70 genes. Mol Ecol 10, 1061–1067. https://doi.org/10.1046/j.1365-294x.2001.01242.x (2001).27.Klobutcher, L. A. & Farabaugh, P. J. Shifty ciliates: frequent programmed translational frameshifting in euplotids. Cell 111, 763–766 (2002).CAS 
    Article 

    Google Scholar 
    28.Lobanov, A. V. et al. Position-dependent termination and widespread obligatory frameshifting in Euplotes translation. Nat Struct Mol Biol 24, 61–68. https://doi.org/10.1038/nsmb.3330 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Coordinators, N. R. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 45, D12–D17. https://doi.org/10.1093/nar/gkw1071 (2017).CAS 
    Article 

    Google Scholar 
    30.Pucciarelli, S. et al. Microbial consortium associated with the antarctic marine ciliate Euplotes focardii: an investigation from genomic sequences. Microb Ecol 70, 484–497. https://doi.org/10.1007/s00248-015-0568-9 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Klobutcher, L. A. et al. Conserved DNA sequences adjacent to chromosome fragmentation and telomere addition sites in Euplotes crassus. Nucleic Acids Res 26, 4230–4240. https://doi.org/10.1093/nar/26.18.4230 (1998).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Aeschlimann, S. H. et al. The draft assembly of the radically organized Stylonychia lemnae macronuclear genome. Genome Biol Evol 6, 1707–1723. https://doi.org/10.1093/gbe/evu139 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Swart, E. C. (personal communication).34.Cavalcanti, A. R., Stover, N. A., Orecchia, L., Doak, T. G. & Landweber, L. F. Coding properties of Oxytricha trifallax (Sterkiella histriomuscorum) macronuclear chromosomes: analysis of a pilot genome project. Chromosoma 113, 69–76. https://doi.org/10.1007/s00412-004-0295-3 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    35.Lozupone, C. A., Knight, R. D. & Landweber, L. F. The molecular basis of nuclear genetic code change in ciliates. Curr Biol 11, 65–74. https://doi.org/10.1016/s0960-9822(01)00028-8 (2001).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Salas-Marco, J. et al. Distinct paths to stop codon reassignment by the variant-code organisms Tetrahymena and Euplotes. Mol Cell Biol 26, 438–447. https://doi.org/10.1128/MCB.26.2.438-447.2006 (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Klobutcher, L. A. Sequencing of random Euplotes crassus macronuclear genes supports a high frequency of +1 translational frameshifting. Eukaryot Cell 4, 2098–2105. https://doi.org/10.1128/EC.4.12.2098-2105.2005 (2005).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Wang, R., Xiong, J., Wang, W., Miao, W. & Liang, A. High frequency of +1 programmed ribosomal frameshifting in Euplotes octocarinatus. Sci Rep 6, 21139. https://doi.org/10.1038/srep21139 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Turanov, A. A. et al. Genetic code supports targeted insertion of two amino acids by one codon. Science 323, 259–261. https://doi.org/10.1126/science.1164748 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Maehigashi, T., Dunkle, J. A., Miles, S. J. & Dunham, C. M. Structural insights into +1 frameshifting promoted by expanded or modification-deficient anticodon stem loops. Proc Natl Acad Sci U S A 111, 12740–12745. https://doi.org/10.1073/pnas.1409436111 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Miceli, C., Ballarini, P., Di Giuseppe, G., Valbonesi, A. & Luporini, P. Identification of the tubulin gene family and sequence determination of one beta-tubulin gene in a cold-poikilotherm protozoan, the antarctic ciliate Euplotes focardii. J Eukaryot Microbiol 41, 420–427. https://doi.org/10.1111/j.1550-7408.1994.tb06100.x (1994).CAS 
    Article 
    PubMed 

    Google Scholar 
    42.Ricci, F. et al. The sub-chromosomic macronuclear pheromone genes of the ciliate Euplotes raikovi: comparative structural analysis and insights into the mechanism of expression. J Eukaryot Microbiol 66, 376–384. https://doi.org/10.1111/jeu.12677 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Wang, R., Liu, J., Di Giuseppe, G. & Liang, A. UAA and UAG may Encode Amino Acid in Cathepsin B Gene of Euplotes octocarinatus. J Eukaryot Microbiol 67, 144–149. https://doi.org/10.1111/jeu.12755 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    44.Heaphy, S. M., Mariotti, M., Gladyshev, V. N., Atkins, J. F. & Baranov, P. V. Novel ciliate genetic code variants including the reassignment of all three stop codons to sense codons in condylostoma magnum. Mol Biol Evol 33, 2885–2889. https://doi.org/10.1093/molbev/msw166 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Swart, E. C., Serra, V., Petroni, G. & Nowacki, M. Genetic codes with no dedicated stop codon: context-dependent translation termination. Cell 166, 691–702. https://doi.org/10.1016/j.cell.2016.06.020 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Roy, B., Leszyk, J. D., Mangus, D. A. & Jacobson, A. Nonsense suppression by near-cognate tRNAs employs alternative base pairing at codon positions 1 and 3. Proc Natl Acad Sci U S A 112, 3038–3043. https://doi.org/10.1073/pnas.1424127112 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Dunn, J. G., Foo, C. K., Belletier, N. G., Gavis, E. R. & Weissman, J. S. Ribosome profiling reveals pervasive and regulated stop codon readthrough in Drosophila melanogaster. Elife 2, e01179. https://doi.org/10.7554/eLife.01179 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Frechin, M., Duchene, A. M. & Becker, H. D. Translating organellar glutamine codons: a case by case scenario?. RNA Biol 6, 31–34. https://doi.org/10.4161/rna.6.1.7564 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    49.Wilcox, M. & Nirenberg, M. Transfer RNA as a cofactor coupling amino acid synthesis with that of protein. Proc Natl Acad Sci U S A 61, 229–236. https://doi.org/10.1073/pnas.61.1.229 (1968).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Detrich, H. W. 3rd., Fitzgerald, T. J., Dinsmore, J. H. & Marchese-Ragona, S. P. Brain and egg tubulins from antarctic fishes are functionally and structurally distinct. J Biol Chem 267, 18766–18775 (1992).CAS 
    Article 

    Google Scholar 
    51.Detrich, H. W. 3rd., Johnson, K. A. & Marchese-Ragona, S. P. Polymerization of Antarctic fish tubulins at low temperatures: energetic aspects. Biochemistry 28, 10085–10093 (1989).CAS 
    Article 

    Google Scholar 
    52.Wloga, D. et al. Glutamylation on alpha-tubulin is not essential but affects the assembly and functions of a subset of microtubules in Tetrahymena thermophila. Eukaryot Cell 7, 1362–1372. https://doi.org/10.1128/EC.00084-08 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Eisen, J. A. et al. Macronuclear genome sequence of the ciliate Tetrahymena thermophila, a model eukaryote. PLoS Biol 4, e286. https://doi.org/10.1371/journal.pbio.0040286 (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Aury, J. M. et al. Global trends of whole-genome duplications revealed by the ciliate Paramecium tetraurelia. Nature 444, 171–178. https://doi.org/10.1038/nature05230 (2006).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    55.Pucciarelli, S. et al. Distinct functional roles of beta-tubulin isotypes in microtubule arrays of Tetrahymena thermophila, a model single-celled organism. PLoS ONE 7, e39694. https://doi.org/10.1371/journal.pone.0039694 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Pucciarelli, S. et al. Tubulin folding: the special case of a beta-tubulin isotype from the Antarctic psychrophilic ciliate Euplotes focardii. Polar Biol 36, 1833–1838. https://doi.org/10.1007/s00300-013-1390-9 (2013).Article 

    Google Scholar 
    57.Pucci, F. & Rooman, M. Physical and molecular bases of protein thermal stability and cold adaptation. Curr Opin Struct Biol 42, 117–128. https://doi.org/10.1016/j.sbi.2016.12.007 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Aqvist, J., Isaksen, G. V. & Brandsdal, B. O. Computation of enzyme cold adaptation. Nat Rev Chem 1, 0051. https://doi.org/10.1038/s41570-017-0051 (2017).CAS 
    Article 

    Google Scholar 
    59.Lesser, M. P. Oxidative stress in marine environments: biochemistry and physiological ecology. Annu Rev Physiol 68, 253–278. https://doi.org/10.1146/annurev.physiol.68.040104.110001 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.McCord, J. M. & Fridovich, I. Superoxide dismutase. An enzymic function for erythrocuprein (hemocuprein). J Biol Chem 244, 6049–6055 (1969).61.McCord, J. M. & Fridovich, I. Superoxide dismutase: the first twenty years (1968–1988). Free Radic Biol Med 5, 363–369 (1988).CAS 
    Article 

    Google Scholar 
    62.Miller, A. F. Superoxide dismutases: ancient enzymes and new insights. FEBS Lett 586, 585–595. https://doi.org/10.1016/j.febslet.2011.10.048 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    63.Benov, L. T. & Fridovich, I. Escherichia coli expresses a copper- and zinc-containing superoxide dismutase. J Biol Chem 269, 25310–25314 (1994).CAS 
    Article 

    Google Scholar 
    64.Steinman, H. M. & Ely, B. Copper-zinc superoxide dismutase of Caulobacter crescentus: cloning, sequencing, and mapping of the gene and periplasmic location of the enzyme. J Bacteriol 172, 2901–2910. https://doi.org/10.1128/jb.172.6.2901-2910.1990 (1990).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Antonyuk, S. V., Strange, R. W., Marklund, S. L. & Hasnain, S. S. The structure of human extracellular copper-zinc superoxide dismutase at 1.7 A resolution: insights into heparin and collagen binding. J Mol Biol 388, 310–326. https://doi.org/10.1016/j.jmb.2009.03.026 (2009).66.Marklund, S. L. Extracellular superoxide dismutase and other superoxide dismutase isoenzymes in tissues from nine mammalian species. Biochem J 222, 649–655. https://doi.org/10.1042/bj2220649 (1984).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Bannister, J. V., Bannister, W. H. & Rotilio, G. Aspects of the structure, function, and applications of superoxide dismutase. CRC Crit Rev Biochem 22, 111–180 (1987).CAS 
    Article 

    Google Scholar 
    68.James, E. R. Superoxide dismutase. Parasitol Today 10, 481–484. https://doi.org/10.1016/0169-4758(94)90161-9 (1994).CAS 
    Article 
    PubMed 

    Google Scholar 
    69.Ferro, D. et al. Cu, Zn superoxide dismutases from Tetrahymena thermophila: molecular evolution and gene expression of the first line of antioxidant defenses. Protist 166, 131–145. https://doi.org/10.1016/j.protis.2014.12.003 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    70.Arnaiz, O. & Sperling, L. ParameciumDB in 2011: new tools and new data for functional and comparative genomics of the model ciliate Paramecium tetraurelia. Nucleic Acids Res 39, D632-636. https://doi.org/10.1093/nar/gkq918 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    71.Fink, R. C. & Scandalios, J. G. Molecular evolution and structure–function relationships of the superoxide dismutase gene families in angiosperms and their relationship to other eukaryotic and prokaryotic superoxide dismutases. Arch Biochem Biophys 399, 19–36. https://doi.org/10.1006/abbi.2001.2739 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    72.Lee, Y. M., Friedman, D. J. & Ayala, F. J. Superoxide dismutase: an evolutionary puzzle. Proc Natl Acad Sci U S A 82, 824–828. https://doi.org/10.1073/pnas.82.3.824 (1985).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Pischedda, A. et al. Antarctic marine ciliates under stress: superoxide dismutases from the psychrophilic Euplotes focardii are cold-active yet heat tolerant enzymes. Sci Rep 8, 14721. https://doi.org/10.1038/s41598-018-33127-1 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Yang, G. et al. Characterization of the first eukaryotic cold-adapted patatin-like phospholipase from the psychrophilic Euplotes focardii: Identification of putative determinants of thermal-adaptation by comparison with the homologous protein from the mesophilic Euplotes crassus. Biochimie 95, 1795–1806. https://doi.org/10.1016/j.biochi.2013.06.008 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    75.Li, J., Zhou, L., Lin, X., Yi, Z. & Al-Rasheid, K. A. Characterizing dose-responses of catalase to nitrofurazone exposure in model ciliated protozoan Euplotes vannus for ecotoxicity assessment: enzyme activity and mRNA expression. Ecotoxicol Environ Saf 100, 294–302. https://doi.org/10.1016/j.ecoenv.2013.08.021 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    76.Prast-Nielsen, S., Huang, H. H. & Williams, D. L. Thioredoxin glutathione reductase: its role in redox biology and potential as a target for drugs against neglected diseases. Biochim Biophys Acta 1262–1271, 2011. https://doi.org/10.1016/j.bbagen.2011.06.024 (1810).CAS 
    Article 

    Google Scholar 
    77.Kabani, M. & Martineau, C. N. Multiple hsp70 isoforms in the eukaryotic cytosol: mere redundancy or functional specificity?. Curr Genomics 9, 338–248. https://doi.org/10.2174/138920208785133280 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.La Terza, A., Miceli, C. & Luporini, P. The gene for the heat-shock protein 70 of Euplotes focardii, an Antarctic psychrophilic ciliate. Antarct. Sci. 16, 23–28. https://doi.org/10.1017/S0954102004001774 (2004).ADS 
    Article 

    Google Scholar 
    79.Chen, X. et al. Genome analyses of the new model protist Euplotes vannus focusing on genome rearrangement and resistance to environmental stressors. Mol Ecol Resour 19, 1292–1308. https://doi.org/10.1111/1755-0998.13023 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Chen, Z. et al. Transcriptomic and genomic evolution under constant cold in Antarctic notothenioid fish. Proc Natl Acad Sci U S A 105, 12944–12949. https://doi.org/10.1073/pnas.0802432105 (2008).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.Li, Y. et al. Comparative transcriptomic analysis reveals gene expression associated with cold adaptation in the tea plant Camellia sinensis. BMC Genomics 20, 624. https://doi.org/10.1186/s12864-019-5988-3 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    82.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.Andrews, S. (2010).84.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19, 455–477. https://doi.org/10.1089/cmb.2012.0021 (2012).MathSciNet 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    85.Nikolenko, S. I., Korobeynikov, A. I. & Alekseyev, M. A. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 14 Suppl 1, S7. https://doi.org/10.1186/1471-2164-14-S1-S7 (2013).86.Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075. https://doi.org/10.1093/bioinformatics/btt086 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    87.Boscaro, V., Husnik, F., Vannini, C. & Keeling, P. J. Symbionts of the ciliate Euplotes: diversity, patterns and potential as models for bacteria-eukaryote endosymbioses. Proc Biol Sci 286, 20190693. https://doi.org/10.1098/rspb.2019.0693 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    88.Serra, V. et al. Morphology, ultrastructure, genomics, and phylogeny of Euplotes vanleeuwenhoeki sp. nov. and its ultra-reduced endosymbiont “Candidatus Pinguicoccus supinus” sp. nov. Sci Rep 10, 20311. https://doi.org/10.1038/s41598-020-76348-z (2020).89.Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res 34, W435-439. https://doi.org/10.1093/nar/gkl200 (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323. https://doi.org/10.1186/1471-2105-12-323 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    91.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    92.Gotz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36, 3420–3435. https://doi.org/10.1093/nar/gkn176 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    93.Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067. https://doi.org/10.1093/bioinformatics/btm071 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    94.Laslett, D. & Canback, B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res 32, 11–16. https://doi.org/10.1093/nar/gkh152 (2004).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    95.Gruber, A. R., Lorenz, R., Bernhart, S. H., Neubock, R. & Hofacker, I. L. The Vienna RNA websuite. Nucleic Acids Res 36, W70-74. https://doi.org/10.1093/nar/gkn188 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.Popenda, M. et al. Automated 3D structure composition for large RNAs. Nucleic Acids Res 40, e112. https://doi.org/10.1093/nar/gks339 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    97.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    98.Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659. https://doi.org/10.1093/bioinformatics/btl158 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    99.Shigematsu, M. et al. YAMAT-seq: an efficient method for high-throughput sequencing of mature transfer RNAs. Nucleic Acids Res 45, e70. https://doi.org/10.1093/nar/gkx005 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Bushnell, B., Rood, J. & Singer, E. BBMerge: accurate paired shotgun read merging via overlap. PLoS ONE 12, e0185056. https://doi.org/10.1371/journal.pone.0185056 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    101.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011 17, 3. https://doi.org/10.14806/ej.17.1.200 (2011).102.Holmes, A. D., Howard, J. M., Chan, P. P. & Lowe, T. M. tRNA Analysis of eXpression (tRAX): A tool for integrating analysis of tRNAs, tRNA-derived small RNAs, and tRNA modifications. (Submitted) (2020).103.Sievers, F. & Higgins, D. G. Clustal omega. Curr Protoc Bioinformatics 48, 3 13 11–16. https://doi.org/10.1002/0471250953.bi0313s48 (2014).104.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    105.Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr Protoc Bioinform. 54, 5 6 1–5 6 37. https://doi.org/10.1002/cpbi.3 (2016).106.Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46, W296–W303. https://doi.org/10.1093/nar/gky427 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    107.Ichikawa, M. et al. Tubulin lattice in cilia is in a stressed form regulated by microtubule inner proteins. Proc Natl Acad Sci U S A 116, 19930–19938. https://doi.org/10.1073/pnas.1911119116 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    108.Chaaban, S. et al. The Structure and Dynamics of C. elegans Tubulin Reveals the Mechanistic Basis of Microtubule Growth. Dev Cell 47, 191–204 e198. https://doi.org/10.1016/j.devcel.2018.08.023 (2018).109.Kikkawa, M. et al. Switch-based mechanism of kinesin motors. Nature 411, 439–445. https://doi.org/10.1038/35078000 (2001).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    110.Howes, S. C. et al. Structural differences between yeast and mammalian microtubules revealed by cryo-EM. J Cell Biol 216, 2669–2677. https://doi.org/10.1083/jcb.201612195 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    111.Ma, M. et al. Structure of the Decorated Ciliary Doublet Microtubule. Cell 179, 909–922 e912. https://doi.org/10.1016/j.cell.2019.09.030 (2019).112.Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001 (2015).ADS 
    Article 

    Google Scholar 
    113.Morrison, T. B., Weis, J. J. & Wittwer, C. T. Quantification of low-copy transcripts by continuous SYBR Green I monitoring during amplification. Biotechniques 24, 954–958, 960, 962 (1998).114.Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29, e45. https://doi.org/10.1093/nar/29.9.e45 (2001).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    115.Pfaffl, M. W., Horgan, G. W. & Dempfle, L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res 30, e36. https://doi.org/10.1093/nar/30.9.e36 (2002).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    A Tunguska sized airburst destroyed Tall el-Hammam a Middle Bronze Age city in the Jordan Valley near the Dead Sea

    Melted quartz grainsCrystalline quartz melts between 1670 °C (tridymite) and 1713 °C (cristobalite), and because quartz is pervasive and easily identified, melted grains serve as an important temperature indicator. At TeH, we observed that unmelted potsherds displayed no melted quartz grains, indicating exposure to low temperatures. On the other hand, most quartz grains on the surfaces of pottery, mudbricks, and roofing clay exhibited some degree of melting, and unmelted quartz grains were rare. Nearly all quartz grains found on broken, unmelted surfaces of potsherds were also unmelted. On melted pottery and mudbricks, melted quartz has an estimated density of 1 grain per 5 mm2.Melted quartz grains at TeH exhibit a wide range of morphologies. Some show evidence of partial melting that only melted grain edges and not the rest of the grain (Figs. 22, 23). Others displayed nearly complete melting with diffusion into the melted Ca–Al–Si matrix of pottery or mudbrick (Fig. 22). Melted quartz grains commonly exhibit vesiculation caused by outgassing (Figs. 22, 23), suggesting that those grains rose above quartz’s melting point of ~ 1713 °C.Figure 22SEM images of melted quartz grains on melted potsherd from the palace. (a) Highly melted quartz grain from the upper surface of melted pottery; shows flow lines of molten quartz in darker ‘neck’ at upper right; (b) manually constructed EDS-based phase map showing 100% quartz grain (green) embedded in Ca–Al–Si matrix of melted pottery (red); blue marks mixing zone between SiO2 and matrix at approximately  > 1713 °C, the melting point of quartz. Yellow arrow points to area depleted in oxygen, indicating high-temperature transformation to elemental Si mixed with melted SiO2. (c) Highly melted quartz grain; (d) manually constructed EDS-based phase map showing diffusion/mixing zone in blue with arrow pointing to bubble, indicating outgassing as grain reached temperatures above its melting point. (e) Quartz grain that has almost completely melted; (f) manually constructed EDS-based phase map showing the small remnant of a melted quartz grain (green) with a wide mixing zone (blue).Full size imageFigure 23SEM images of melted quartz grains on melted mudbrick from the palace. (a) Highly melted quartz grain; (b) manually constructed EDS-based phase map indicates center is pure SiO2 surrounded by melted mudbrick. Arrow points to vesicles indicating outgassing as grain temperature rose above ~ 1713 °C, the melting point of quartz. (c) The surface of a flattened quartz grain showing flow marks toward the upper right. High temperatures are required to lower the viscosity sufficiently for quartz to flow. (d) Manually constructed EDS-based phase map with an arrow pointing to vesicles indicating outgassing at high temperatures. (e) Close up of grain in panel ‘c’ showing flow marks (schlieren) at arrows. (f) Shattered, melted quartz splattered onto mudbrick meltglass; (g) manually constructed EDS-based phase map indicating that the blue area is SiO2; the yellow area is a shattered, thermally altered Fe-oxide grain.Full size imageAn SEM–EDS elemental map of one melted grain showed that the quartz had begun to dissociate into elemental Si (Fig. 22b). Another grain (Fig. 23c–e) displayed flow marks consistent with exposure to temperatures above 1713 °C where the viscosity of quartz falls low enough for it to flow easily. Another SEM–EDS analysis confirmed that one agglutinated mass of material is 100 wt.% SiO2 (Fig. 23f, g), suggesting that this polycrystalline quartz grain shattered, melted, and partially fused again.Discussion of melted quartzMoore et al.17 reported that during heating experiments, many quartz grains  50-µm-wide remained visually unaltered up to ~ 1700 °C. By 1850 °C, all quartz grains fully melted. These experiments establish a particle-size dependency and confirm confirmed the melting point for  > 50-µm-wide TeH quartz grains between ~ 1700–1850 °C. Melted  > 50-µm-wide quartz grains on the surfaces of melted pottery and mudbrick from the TeH destruction layer indicate exposure to these unusually high temperatures  > 1700 °C.Previously, Thy et al.70 proposed that glass at Abu Hureyra did not form during a cosmic impact, but rather, formed in biomass slag that resulted from thatched hut fires. However, Thy et al. did not determine whether or not high-temperature grains existed in the biomass slag. To test that claim, Moore et al.17 analyzed biomass slag from Africa and found only low-temperature melted grains with melting points of ~ 1200 °C, consistent with a temperature range for biomass slag of 1155–1290 °C, as reported by Thy et al.71. Upon testing the purported impact glass from Abu Hureyra, Moore et al.17 discovered high-temperature mineral grains that melt in the range of 1713° to  > 2000 °C, as are also found in TeH glass. These test results suggest that the melted glass from Abu Hureyra must have been exposed to higher temperatures than those associated with fires in thatched huts. Because of the presence of high-temperature minerals at TeH, we conclude that, as at Abu Hureyra, the meltglass could not have formed simply by burning thatched huts or wood-roofed, mudbrick buildings.Melted Fe- and Si-rich spherulesThe presence of melted spherulitic objects (“spherules”) has commonly been used to help identify and investigate high-temperature airburst/impact events in the sedimentary record. Although these objects are referred to here as “spherules,” they display a wide range of other impact-related morphologies that include rounded, sub-rounded, ovate, oblate, elongated, teardrop, dumbbell, and/or broken forms17,72,73,74,75,76,77,78,79,80,81,82. Optical microscopy and SEM–EDS are commonly used to identify and analyze spherules and the processes by which they are formed. Care is needed to conclusively distinguish high-temperature spherules produced by cosmic impacts from other superficially similar forms. Other such objects that frequently occur in sediments include anthropogenic spherules (typically from modern coal-fired power plants), authigenic framboids (Supporting Information, Fig. S7), rounded detrital magnetite, and volcanic spherules.Spherules in TeH sediment were investigated from stratigraphic sequences that include the MB II destruction layer at four locations: palace, temple, ring road, and wadi (Fig. 24). For the palace (Field UA, Square 7GG), the sequence spanned 28 cm with 5 contiguous samples of sediment ranging from 3-cm thick for the MB II destruction layer to 13-cm thick for some outlying samples. In the palace, 310 spherules/kg (Fig. 24d) were observed in the destruction layer with none found in samples above and below this layer. For the temple (Field LS, Square 42J), 5 continuous samples spanned 43 cm and ranged in thickness from 6 to 16 cm; the MB II layer contained ~ 2345 Fe- and Si-rich spherules/kg with 782/kg in the sample immediately below and none at other levels (Fig. 24c). Six contiguous samples from the ring road (Field LA, Square 28 M) spanned 30 cm with all 5 cm thick; the MB II destruction layer at this location contains 2150 spherules/kg with none detected in younger or older samples (Fig. 24b). Five discontinuous samples from the wadi spanned 170 cm, ranging from 10-cm thick for the destruction layer up to 20-cm thick for other samples; the MB II destruction layer at this location contained 2780 spherules/kg with none in samples from other levels (Fig. 24a, Supporting Information, Table S3). Notably, when melted mudbrick from the ring road was being mounted for SEM analysis, numerous loose spherules were observed within vesicles of the sample, confirming a close association between the spherules and meltglass. At all four locations, the peaks in high-temperature spherule abundances occur in the MB II destruction layer dating to ~ 1650 BCE.Figure 24Spherule abundances. (a)–(d). Number per kg for Fe- and S-rich spherules from 4 locations. Depths are in cm above or below the bottom of the destruction layer.Full size imageSEM images of spherules are shown in Figs. 25, 26, 27 and 28, and compositions are listed in Supporting Information, Table S4. The average spherule diameter was 40.5 µm with a range of 7 to 72 µm. The dominant minerals were Fe oxides averaging 40.2 wt.%, with a range of up to 84.1 wt.%; elemental Fe with a range of up to 80.3 wt.%; SiO2 averaging 20.9 wt.%, ranging from 1.0 to 45.2 wt.%; Al2O3 averaging 7.8 wt.% with a range of up to 15.6 wt.%; and TiO2 averaging 7.1 wt.% with a range of up to 53.1 wt.%. Fourteen spherules had compositions  > 48 wt.% of oxidized Fe, elemental Fe, and TiO2; five spherules contained  75 wt.% Fe with no Ti. Eight of 23 spherules analyzed contained detectable levels of Ti at up to 53.1 wt.%.Figure 25SEM images of mostly silica-rich spherules from TeH. (a)–(d) Representative spherules from the ring road on the lower tall. SEM images of iron-rich spherules. (e)–(f) Fe-rich spherules from the temple complex. (g) temple spherule containing ~ 3.7 wt.% Cr. (h) Broken, vesicular spherule from temple containing 1.4 wt.% Ni and 3.7 wt.% Cr. SEM images of titanium-rich spherules. Ti content of these ranges from 18.9 to 1.2 wt.%, averaging 10.7 wt.%. (i)–(k) Spherules from the ring road. (l) Spherule from the wadi site.Full size imageFigure 26SEM image of rare-earth (REE) spherule. (a) REE-rich 72-µm-wide spherule from the palace, dominantly composed of Fe, La, Ce, and O. (b) Close up of REE blebs found on the spherule. (c)–(f) SEM–EDS elemental maps showing composition. La = 15.6 wt.% and Ce = 21.0 wt.%. Ce is enriched over Fe and La in the middle part of the spherule, as seen in panels ‘d’ through ‘f’.Full size imageFigure 27SEM images of a spherule mainly composed of Fe and Si. (a) Fe–Ti-rich 54-µm-wide spherule from the palace. Spherule displays a protrusion to the left, suggesting aerodynamic shaping when molten, after which the tail detached. (b) A focused ion beam (FIB) was used to section the spherule, revealing inclusions of wassonite or titanium sulfide (TiS; yellow arrows) that are lighter-colored than the matrix. (c)–(f) Color-coded SEM–EDS elemental maps, showing the distribution of Ti, S, Si, and Fe and the location of the TiS grains. The spherule is dominantly composed of Fe and Si with minor amounts of Ti and S found in TiS inclusions.Full size imageFigure 28Fe-rich spherules embedded in meltglass. (a) Optical photomicrograph of a 167-µm-wide piece of meltglass with embedded Fe-rich spherules. (b) SEM image of same grain as in panel ‘a’. Melted quartz grain (Qtz) is embedded in Ca–Al–Si-rich matrix, which has the same composition as melted mudbrick. (c) SEM close-up image of the boxed area and panel ‘b’, showing splattered Fe-rich spherule.Full size imageTwo unusual spherules from the palace contain anomalously high percentages of rare-earth elements (REEs) at  > 37 wt.% of combined lanthanum (La), and cerium (Ce) (Fig. 26), as determined by preliminary measurements using SEM–EDS. Minor oxides account for the rest of the spherules’ bulk composition (Table S1).One 54-µm-wide sectioned spherule contains titanium sulfide (TiS) with a melting point of ~ 1780° C. TiS, known as wassonite, was first identified in meteorites (Fig. 27) and has been reported in impact-related material17,81,83. However, TiS sometimes occurs as an exsolution product forming fine networks in magnetite and ilmenite and can be of terrestrial origin.One unusual piece of 167-µm-wide Ca–Al–Si meltglass contains nearly two dozen iron oxide spherules on its surface (Fig. 28). The meltglass contains a completely melted quartz grain as part of the matrix (Fig. 28b). Most of the spherules appear to have been flattened or crushed by collision with the meltglass while they were still partially molten (Fig. 28c).Discussion of spherules and meltglassMelted materials from non-impact-related combustion have been reported in multiple studies. Consequently, we investigated whether Ca-, Fe-, and Si-rich spherules and meltglass (mudbrick, pottery, plaster, and roofing clay) may have formed normally, rather than from a cosmic impact event. For example, (i) glassy spherules and meltglass are known to form when carbon-rich biomass smolders below ground at ~ 1000° to 1300 °C, such as in midden mounds71. They also form in buried peat deposits84, underground coal seams85, burned haystacks86, and in large bonfires, such as at the Native American site at Cahokia, Illinois, in the USA87. (ii) Also, ancient fortifications (hillforts) in Scotland and Sweden, dating from ~ 1000 BCE to 1400 AD, have artificially vitrified walls that melted at temperatures of ~ 850° to 1000 °C88. (iii) Partially vitrified pottery and meltglass derived from the melting of wattle and daub (thatch and clay) with estimated temperatures of ~ 1000 °C have been reported in burned houses of the Trypillia culture in Ukraine89,90. (iv) Vitrified mudbricks and pottery that melted at 17 investigated biomass glass from midden mounds in Africa and found no high-temperature minerals. For this contribution, we used SEM–EDS to examine aluminosilicate meltglass from an underground peat fire in South Carolina, USA; meltglass in coal-fired fly ash from New Jersey, USA; and mining slag from a copper mine in Arizona, USA. All these meltglass examples display unmelted quartz and contain no other high-temperature melted grains, consistent with low-temperature melting at  97% wt.% FeO, as are found at TeH. Nor can these low temperatures produce meltglass and spherules embedded with melted zircon (melting point = 1687 °C), chromite (2190 °C), quartz (1713 °C), platinum (1768 °C), and iridium (2466 °C). Moore et al.17 confirmed that the melting of these high-temperature minerals requires minimum temperatures of ~ 1500° to 2500 °C.This evidence demonstrates that although the matrix of the spherules and meltglass at TeH likely experienced incipient melting at temperatures lower than ~ 1300 °C, this value represents only the minimum temperature of exposure, because the high-temperature minerals embedded in them do not melt at such low temperatures. Instead, the spherules and meltglass at TeH must have reached temperatures greater than ~ 1300 °C, most likely involving brief exposure to ambient temperatures of ~ 2500 °C, the melting point of iridium. These temperatures far exceed those characteristic of city fires and other types of biomass burning. In summary, all of this evidence is consistent with very high temperatures known during cosmic impacts but inconsistent with other known natural causes.Calcium carbonate spherules and plasterIn sediments of the destruction layer, we observed amber-to-off-white-colored spherules (Fig. 29) at high concentrations of ~ 240,000/kg in the palace, ~ 420/kg in the temple, ~ 60/kg on the ring road, and ~ 910/kg in the wadi (Supporting information, Table S2). In all four profiles, the spherules peak in the destruction layer with few to none above or below. Peak abundances of calcium carbonate spherules are closely associated with peak abundances of plaster fragments, which are the same color. By far the most spherules (~ 250× more) occurred in the destruction layer of the palace, where excavations showed that nearly every room and ceiling was surfaced with off-white lime-based plaster. Excavators uncovered high-quality lime plaster fragments still adhering to mudbricks inside the MB II palace complex, and in one palace room, we uncovered fragments of melted plaster (Fig. 29e). In contrast, lime plaster was very rarely used in buildings on the lower tall, including those near the temple.Figure 29Images of calcium carbonate spherules and melted plaster from TeH. (a) Photomicrographs of translucent, amber-colored CaCO3 spherules from the destruction layer in the palace. (b) SEM image of 83-µm carbonate spherule with impact or outgassing crater at arrow. (c) Photomicrograph of ~ 2-mm-wide piece of partially melted palace plaster from oxygen/propylene torch test, showing incipient melting at 1500 °C. Arrows point to hemispheric droplets emerging as spherules. (d) 142-µm cluster of 8 carbonate spherules with apparent impact or outgassing crater at arrow. (e) 64 × 30 mm piece of melted plaster that broke off the palace wall and became melted. It is composed only of calcium, carbon, and oxygen.Full size imageTo explore a potential connection between plaster and spherules, we performed SEM–EDS on samples of the palace plaster. Comparison of SEM–EDS analyses shows that the plaster composition has a  > 96% similarity to the spherule composition: CaCO3 = 71.4 wt.% in plaster versus 68.7 wt.% in the spherules; elemental C = 23.6 versus 26.3 wt.%; SiO2 = 2.4 versus 1.8 wt.%; MgO = 1.7 versus 2.0 wt.%; and SO3 = 0.94 versus 1.2 wt.%. The high carbon percentage and low sulfur content indicate that the plaster was made from calcium carbonate and not gypsum (CaSO4·2H2O). SEM imaging revealed that the plaster contains small plant parts, commonly used in plaster as a binder, and is likely the source of the high abundance of elemental C in the plaster. Inspection showed no evidence of microfossils, such as coccoliths, brachiopods, and foraminifera. The morphology of the spherules indicates that they are not authigenic or biological in origin.Discussion of carbonate plaster and spherulesOne of the earliest known uses of CaCO3-based plaster was in ~ 6750 BCE at Ayn Ghazal, ~ 35 km from TeH in modern-day Amman, Jordan97. At that site, multi-purpose lime plaster was used to make statues and figurines and to coat the interior walls of buildings. Because the production of lime-based plaster occurred at least 3000 years before TeH was destroyed, the inhabitants of TeH undoubtedly were familiar with the process. Typically, lime powder was produced in ancient times by stacking wood/combustibles interspersed with limestone rocks and then setting the stack on fire. Temperatures of ~ 800–1100 °C were required to transform the rocks into crumbly chalk, which was then mixed with water to make hydrated lime and plastered onto mudbrick walls97.At TeH, fragments of CaCO3-based plaster are intermixed in covarying abundances with CaCO3-based spherules with both compositions matching to within 96%. This similarity suggests that the carbonate spherules are derived from the plaster. We infer that the high-temperature blast wave from the impact event stripped some plaster from the interior walls of the palace and melted some into spherules. However, it is difficult to directly melt CaCO3, which gives off CO2 at high temperatures and decomposes into lime powder. We investigated this cycle in a heating experiment with an oxygen/propylene torch and found that we could decompose the plaster at ~ 1500 °C, the upper limit of the heating test, and begin incipient melting of the plaster. The heated plaster produced emergent droplets at that temperature but did not transform into free spherules (Supporting Information, Text S2).Similar spherules have been reported from Meteor Crater, where spherules up to ~ 200 μm in diameter are composed entirely of CaCO3 formed from a cosmic impact into limestone98,99. One of several possible hypotheses for TeH is that during the impact event, the limestone plaster converted to CaO with an equilibrium melting point of 2572 °C. However, it is highly likely that airborne contaminants, such as sodium and water vapor, reacted with the CaO and significantly lowered the melting point, allowing spherule formation at ≥ 1500 °C.The proposed chemical sequence of events of plaster formation and the later impact are as follows:

    1.

    Limestone was heated to ~ 800–1100 °C, decomposing to quicklime:

    $${text{CaCO}}_{{3}} to {text{ CaO }} + {text{ CO}}_{{2}}$$

    2.

    Quicklime was mixed with water to make a wet plaster:$${text{CaO }} + {text{ H}}_{{2}} {text{O }} to {text{ Ca}}left( {{text{OH}}} right)_{{2}}$$

    3.

    The plaster hardened and slowly absorbed CO2 to revert to CaCO3:$${text{Ca}}left( {{text{OH}}} right)_{{2}} + {text{ CO}}_{{2}} to {text{ H}}_{{2}} {text{O }} + {text{ CaCO}}_{{3}}$$

    4.

    The high-temperature impact event melted some plaster into spherules:$${text{CaCO}}_{{3}} to {text{ CaO }}left( {{text{spherules}}} right) , + {text{ CO}}_{{2}} left( { > {15}00^circ {text{C}}} right)$$

    5.

    CaO spherules slowly absorbed CO2 to revert to CaCO3:$${text{Ca }} + {text{ CO}}_{{2}} to {text{ CaCO}}_{{3}} left( {text{as spherules}} right)$$

    General discussion of all spherulesAccording to the previous investigations17,72,81,82, Fe-rich spherules such as those found at TeH typically melt at  > 1538 °C, the melting point of iron (Table 1). Because of the presence of magnetite (Fe3O4) in the REE spherule, its melting point is inferred to be  > 1590 °C (Table 1). The Si-rich spherules are similar in composition to TeH sediment and mudbrick, and thus, we propose that they were derived from the melting of these materials at  > 1250 °C. The carbonate-rich spherules likely formed at  > 1500 °C.Several studies describe a mechanism by which spherules could form during a low-altitude cosmic airburst100,101. When a bolide enters Earth’s atmosphere, it is subjected to immense aerodynamic drag and ablation, causing most of the object to fragment into a high-temperature fireball, after which its remaining mass is converted into a high-temperature vapor jet that continues at hypervelocity down to the Earth’s surface. Depending on the altitude of the bolide’s disruption, this jet is capable of excavating unconsolidated surficial sediments, melting them, and ejecting the molten material into the air as Si- and Fe-rich spherules and meltglass. This melted material typically contains a very low percentage (17.Melted zircons in pottery and mudbricks were observed (Fig. 30) at an estimated density of 1 grain per 20 mm2. On highly melted surfaces, nearly all zircons showed some degree of melting. In contrast, nearly all zircons found on broken interior surfaces were unmelted (Fig. 30d), except those within ~ 1 mm of melted surfaces. This implies that the temperature of the surrounding atmosphere was higher than the internal temperatures of the melting objects. Unmelted potsherds displayed only unmelted minerals.Figure 30SEM images of melted zircon grains. (a) Melted TeH zircon grain with bubbles at yellow arrow due to high-temperature dissociation and/or entrapped porosity. (b) Melted TeH zircon grain decorated with bubbles along the fracture line at upper arrow; arrows labeled “Bd” point to bright granular baddeleyite, ZrO2, formed during the high-temperature dissociation of zircon. (c) Almost fully melted TeH zircon grain mixing into the Ca–Al–Si matrix. (d) A typical unmelted zircon grain from TeH with straight, euhedral edges. Grain shows cracks on the top surface from possible thermal or mechanical damage. (e) For comparison, from cosmic airburst/impact at Dakhleh Oasis in Egypt: melted zircon decorated with lines of bubbles (arrow).Full size imageThe melted zircons in TeH materials exhibit a wide range of morphologies. Most showed evidence of sufficient melting to alter or destroy the original distinctive, euhedral shape of the grains. Also, the grains were often decorated with vesicles that were associated with fractures (Fig. 30a, c).Stoichiometric zircon contains 67.2 wt.% and 32.8 wt.% ZrO2 and SiO2 respectively, but in several TeH samples, we observed a reduction in the SiO2 concentration due to a loss of volatile SiO from the dissociation of SiO2. This alteration has been found to occur at 1676 °C, slightly below zircon’s melting point of 1687 °C103. This zircon dissociation leads to varying ZrO2:SiO2 ratios and to the formation of distinctive granular textures of pure ZrO2, also known as baddeleyite104 (Figs. 30, 31, 32). With increasing time at temperature, zircon will eventually convert partially or completely to ZrO2. Nearly all zircons observed on the surfaces of melted materials were either melted or showed some conversion to baddeleyite. We observed one zircon grain (Fig. 32d–e) displaying granular ZrO2 associated with three phases that span a wide range of SiO2 concentrations, likely formed at temperatures above 1687 °C. This extreme temperature and competing loss of SiO over an inferred duration of only several seconds led to complex microstructures, where grains melted, outgassed, and diffused into the surrounding matrix.Figure 31SEM images of other melted zircon grains in palace potsherd. (a) Two melted zircon grains adjacent to a previously discussed melted quartz grain; (b) close-up of same zircon grains; (c) manually constructed EDS-based phase map showing baddeleyite grains in green. The blue area represents melted zircon, while the red background represents the Ca–Al–Si matrix of the melted pottery. (d) Manually constructed EDS-based phase map of zircon grain showing small baddeleyite grains in green at the top.Full size imageFigure 32SEM images of melted zircon grains in mudbrick meltglass from the palace. (a) Thermally distorted zircon grain with a “hook” that resulted from the flow of molten material at  > 1687 °C; the darker area represents unrelated debris on top of zircon. (b) Manually constructed EDS-based phase map showing baddeleyite grains (Bd = ZrO2) in green, zircon in blue, and melted mudbrick in red. (c) Zircon grain showing limited thermal alteration, yet sufficient to cause dissociation into bright baddeleyite grains at ~ 1676 °C. (d) Zircon grain exhibiting three phases of thermal alteration, as shown in detail in (e), where a manually constructed EDS-based phase map demonstrates that high temperatures caused bubbling in the center band of zircon (purple = Hi) producing sub-micron-sized grains of baddeleyite (e.g., at arrow). Medium temperatures caused zircon to melt and flow (blue = Lo), and lower temperatures at the left end of grain produced thermal cracks (medium blue = Med). The green area marks the high-Si diffusion zone resulting from the dissociation of zircon. (f) Zircon grain from TeH has been fully converted to granular baddeleyite.Full size imageDiscussion of melted zirconZircon grains have a theoretical, equilibrium melting point of ~ 1687 °C. Under laboratory heating17, zircon grains showed no detectable alteration in shape at ~ 1300 °C but displayed incipient melting of grain edges and dissociation to baddeleyite beginning at ~ 1400 °C with increasing dissociation to 1500 °C17. Most zircon grains  120 µm were still recognizable but displayed considerable melting17. These experiments establish a lower melting range for TeH zircon grains of ~ 1400° to 1500 °C.Patterson105 showed that zircon dissociation becomes favorable above 1538 °C and particles between 1 and 100 µm in size melted and dissociated when passing through a plasma, forming spherules with various amounts of SiO2 glass containing ZrO2 crystallites ranging in size from 5 nm to 1 µm. The majority of zircon crystals were monoclinic, but tetragonal ZrO2 was observed for the smaller crystallite sizes. Residence times were in the order of 100 ms, and the specific ZrO2 to SiO2 ratio within each spherule depended on the particle’s time at temperature106.Bohor et al.104 presented images of impact-shocked zircons from the K-Pg impact event at 66 Ma that are morphologically indistinguishable from those at TeH. Decorated zircon grains are uncommon in nature but commonly associated with cosmic impact events, as evidenced by two partially melted zircons from the known airburst/impact at Dakhleh Oasis, Egypt (Fig. 30e). The presence of bubbles indicates that temperatures reached at least 1676 °C, where the zircon began to dissociate and outgas. Similar dissociated zircon grains also have been found in tektite glass and distal fallback ejecta (deposited from hot vapor clouds). Granular baddeleyite-zircon has been found in the ~ 150-km-wide K-Pg impact crater107 and the 28-km-wide Mistatin Lake crater in Canada107. The dissociation of zircon requires high temperatures of ~ 1676 °C104, implying that TeH was exposed to similar extreme conditions.Melted chromite grainsExamples of melted chromite, another mineral that melts at high temperatures, were also observed. Thermally-altered chromite grains were observed in melted pottery, melted mudbricks, and melted roofing clay from the palace. Their estimated density was 1 grain per 100 mm2, making them rarer than melted zircon grains. The morphologies of chromite grains range from thermally altered (Fig. 33a) to fully melted (Fig. 33b, d). One chromite grain from the palace displays unusual octahedral cleavage or shock-induced planar fractures (Fig. 33b). The typical chemical composition for chromite is 25.0 wt.% Fe, 28.6 wt.% O, and 46.5 wt.% Cr, although the Cr content can vary from low values to ~ 68 wt.%. SEM images reveal that, as chromite grains melted, some Cr-rich molten material migrated into and mixed with the host melt, causing an increase in Cr and Fe, and corresponding depletion of Si. The ratio of Cr to Fe in chromite affects its equilibrium melting point, which varies from ~ 1590 °C for a negligible amount of Cr up to ~ 2265 °C for ~ 46.5 wt.% Cr as in chromite or chromian magnetite ((Fe)Cr2O4), placing the melting point of TeH chromite at close to 2265 °C.Figure 33SEM images of melted chromite grains found on a melted potsherd from the palace. (a) Shattered, polycrystalline chromite grain that appears to have become agglutinated while molten. (b) Melted chromite grain, displaying cleavage (lamellae) suggestive of thermal and/or mechanical shock metamorphism at ~ 12 GPa; (c) close-up image showing angles between three sets of crystalline cleavage; (d) manually constructed EDS-based phase map showing chromite (purple) embedded in Ca–Al–Si matrix. The lines mark three sets of cleavage extending across the entire grain. A melt tail merging with the matrix is observed to trail off to the upper right of the grain at arrow.Full size imageDiscussion of melted chromiteChromite grains theoretically melt at ~ 2190 °C. Moore et al.17 reported the results of heating experiments in which chromite grains in bulk sediment showed almost no thermal alteration up to ~ 1500 °C (Supporting Information, Fig. S8). At temperatures of ~ 1600 °C and ~ 1700 °C, the shapes of chromite grains were intact but exhibited limited melting of grain edges. These results establish a range of ~ 1600° to 1700 °C for melting chromite grains.Because chromite typically does not exhibit cleavage, the grain exhibiting this feature is highly unusual. Its origin is unclear but there are several possibilities. The cleavage may have resulted from exsolution while cooling in the source magma. Alternately, the lamellae may have resulted from mechanical shock during a cosmic impact, under the same conditions that produced the shocked quartz, as reported by Chen et al.108 for meteorites shocked at pressures of ~ 12 GPa. Or they may have been formed by thermal shock, i.e., rapid thermal loading followed by rapid quenching. This latter suggestion is supported by the observation that the outside glass coating on the potsherd does not exhibit any quench crystals, implying that the cooling progressed very rapidly from liquid state to solid state (glass). This is rare in terrestrial events except for some varieties of obsidian, but common in melted material produced by atomic detonations (trinitite), lightning strikes (fulgurites), and cosmic airburst/impacts (meltglass)81. More investigations are needed to determine the origin of the potentially shocked chromite.Nuggets of Ir, Pt, Ru, Ni, Ag, Au, Cr, and Cu in meltglassUsing SEM–EDS, we investigated abundances and potential origins (terrestrial versus extraterrestrial) of platinum-group elements (PGEs) embedded in TeH meltglass, in addition to Ni, Au, and Ag. Samples studied include melted pottery (n = 3); melted mudbrick (n = 6); melted roofing clay (n = 1), and melted lime-based building plaster (n = 1). On the surfaces of all four types of meltglass, we observed melted metal-rich nuggets and irregularly shaped metallic splatter, some with high concentrations of PGEs (ruthenium (Ru), rhodium (Rh), palladium (Pd), osmium (Os), iridium (Ir), and platinum (Pt)) and some nuggets enriched in silver (Ag), gold (Au), chromium (Cr), copper (Cu), and nickel (Ni) with no PGEs (Figs. 34, 35). Importantly, these metal-rich nuggets were observed only on the top surfaces of meltglass and not inside vesicles or on broken interior surfaces.Figure 34SEM images of nuggets of melted metals in mudbrick meltglass from the palace. (a)–(c) Pt-dominant TeH nuggets enriched in ruthenium (Ru), rhodium (Rh), palladium (Pd), osmium (Os), iridium (Ir), and platinum (Pt). (d)–(f) Fe-dominant TeH splatter is also enriched in PGEs. (g)–(i) Nuggets enriched in varying percentages and combinations of nickel (Ni), chromium (Cr), copper (Cu), and silver (Ag).Full size imageFigure 35Average composition of selected metal-rich nuggets from the palace. (a-h) Silver (Ag), gold (Au), chromium (Cr), copper (Cu), iridium (Ir), nickel (Ni), platinum (Pt), and ruthenium (Ru), showing wt.% in selected nuggets from the destruction layer of the palace (7GG).Full size imageUsing SEM–EDS, we identified variable concentrations and assemblages of PGEs. The metallic particles appear to have melted at high temperatures based on the minimum melting points of the elements: iridium at 2466 °C; platinum = 1768 °C; and ruthenium = 2334 °C, indicating a temperature range of between approximately 1768° and 2466 °C. Our investigations also identified two PGE groups, one with nuggets in which Pt dominates Fe and the other with metallic splatter in which Fe dominates Pt.Pt-dominant nuggetsWe conducted 21 measurements on Pt-dominant TeH nuggets on meltglass (Fig. 34a–c). The nuggets average ~ 5 µm in length (range 1–12 µm) with an estimated concentration of 1 nugget per 10 mm2. For these nuggets, Fe concentrations average 1.0 wt.%, Ir = 6.0 wt.%, and Pt = 44.9 wt.% (Supporting Information, Tables S6, S7). The presence of PGEs was confirmed by two SEM–EDS instruments that verified the accurate identification of PGEs through analyses of several blanks that showed no PGE content. Some concentrations are low ( Pt or Pt  > Fe were found to be consistent between the two instruments.To determine the source of TeH nuggets and splatter, we constructed ternary diagrams. Terrestrial PGE nuggets are commonly found in ore bodies that when eroded, can become concentrated in riverine placer deposits, including those of the Jordan River floodplain. To compare Fe–Ir–Pt relationships among the TeH nuggets, we compiled data from nearby placer deposits in Greece109, Turkey110,111, and Iraq112, along with distant placers in Russia113,114,115, Canada116, and Alaska, USA117,118. The compilation of 109 Pt-dominant placer nuggets indicates that the average Fe concentration is 8.2 wt.%, Ir = 2.9 wt.%, and Pt = 80.3 wt.%. For the Ir-dominant placer nuggets (n = 104), Fe = 0.4 wt.%, Ir = 47.8 wt.%, and Pt = 5.3 wt.% (Supporting Information, Tables S6, S7). The ternary diagrams reveal that the values for Pt-dominant TeH nuggets overlap with Pt-dominant terrestrial placer nuggets but the Fe-dominant splatter is dissimilar (Fig. 36a).Figure 36Ternary diagrams for PGE-rich grains. Comparison of Fe–Ir–Pt ratios of PGE-rich nuggets fused into the surfaces of TeH meltglass. There are two populations of TeH nuggets (red diamonds): Pt-dominant at #1 (top) and Fe-dominant at #2 (bottom left). (a) TeH Pt-dominant nugget group #1 (red diamonds) overlaps Pt-dominant but not Ir-dominant nuggets (blue circles) from placers and ophiolite deposits in Greece, Turkey, Iraq, Russia, Canada, and the USA. The Fe-dominant TeH nugget group #2 is geochemically dissimilar to all known placer nuggets, suggesting that these nuggets are not placer-derived. (b) TeH nuggets (red diamonds) compared to nuggets in carbonaceous chondrites (light gray circles) and nuggets in cosmic spherules (dark gray circles). Pt-dominant TeH nuggets in group #1 are a poor match, but Fe-dominant TeH splatter is an excellent match with chondritic meteorites and cosmic spherules, suggesting that they may be extraterrestrial in origin and that the impactor may have been a chondrite. (c) TeH nuggets (red diamonds) are a poor match for most nuggets in iron meteorites (purple circles), but an excellent match for nuggets found in comets (green circles). These data suggest that Fe-dominant PGE nuggets at TeH may have originated from cometary material. (d) Semi-log comparison of PGEs ruthenium (Ru), rhodium (Rh), palladium (Pd), osmium (Os), iridium (Ir), and platinum (Pt), normalized to CI chondrites. TeH Fe-dominant splatter (red line) is an excellent match for PGE nuggets in carbonaceous chondrites (blue line), cosmic spherules (purple line), micrometeorites (dark blue line), and iron meteorites (gray line). In contrast, TeH PGE nuggets are a poor match for bulk material from CI-normalized CV-type chondrites (e.g., Allende; orange line) and CM-type chondrites (e.g., Murchison; brown line).Full size imageFe-dominant splatterWe made 8 measurements on TeH Fe-dominant PGE splatter (Fig. 34d–f). The metal-rich areas average ~ 318 µm in length (range 20–825 µm) with an estimated concentration of 1 PGE-rich bleb per mm2, 100× more common than the TeH nuggets. Average concentrations are Fe = 17.5 wt.%, Ir = 4.7 wt.%, and Pt = 1.5 wt.%.We explored a potential extraterrestrial origin by constructing ternary diagrams for comparison of TeH Fe-dominant splatter with known meteorites and comets (Fig. 36b, c). We compiled data for 164 nuggets extracted from carbonaceous chondritic meteorites (e.g., Allende, Murchison, Leoville, and Adelaide)119,120,121,122, seafloor cosmic spherules123,124, iron meteorites122,125, Comet Wild 2126, and cometary dust particles126. For average weight percentages, see Supporting Information, Tables S6, S7. The Fe-dominant TeH splatter (Fig. 36b) closely matches nuggets from carbonaceous chondrites and cosmic spherules but is a weak match for most iron meteorites (Fig. 36c). In addition, the TeH nuggets are similar to four cometary particles, two of which were collected during the Stardust flyby mission of Comet Wild 2 in 2004126. For average weight percentages, see Supporting Information, Tables S6, S7.To further explore an extraterrestrial connection for TeH Fe-dominant splatter, we compiled wt.% data for TeH PGEs (Rh, Ru, Pd, Os, Ir, and Pt) and normalized them to CI chondrites using values from Anders and Grevasse127. We compared those values to CI-normalized nuggets in carbonaceous chondrites, including CV-type chondrites (e.g., Allende) and CM types (e.g., Murchison)119,120,122,128,129,130,131, seafloor cosmic spherules124, micrometeorites123, and iron meteorites122,125. These results are shown in Fig. 36d.The TeH Fe-dominant splatter closely matches all types of extraterrestrial material with a similar pattern among all data sets: Pd has the lowest normalized values and Os and/or Ir have the highest, closely followed by Pt. The TeH splatter was also compared to the CI-normalized wt.% of bulk meteoritic material from CV- and CM-type chondrites (Fig. 36d). The composition of TeH splatter shows poor correlation with bulk chondritic materials, although the splatter is an excellent geochemical match with the PGE nuggets inside them. In summary, the CI normalization of PGEs suggests an extraterrestrial origin for the Fe-dominant TeH splatter, just as the ternary diagrams also suggest an extraterrestrial source. The correspondence of these two independent results suggests that the quantification of PGEs is sufficiently accurate in this study.Another unusually abundant element, Mo, is also associated with Fe-dominant splatter but not with Pt-dominant nuggets. Mo averages 0.3 wt.% with up to 1.1 wt.% detected in Fe-dominant splatter but with none detected in TeH Pt-dominant nuggets. Mo also is not reported in any terrestrial placer nuggets and occurs in low concentrations (less than ~ 0.02 wt.%) in iron meteorites. In contrast, Mo is reported at high concentrations in PGE nuggets from carbonaceous chondrites (~ 11.5 wt.%), cosmic spherules (0.6 wt.%), and cometary material (5.8 wt.%). Thus, the Mo content of TeH splatter appears dissimilar to terrestrial material but overlaps values of known cosmic material, suggesting an extraterrestrial origin.Based on the volume and weight of the meltglass, we estimate that the extraterrestrial-like metallic TeH Fe-dominant splatter represents  More