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Fig. 12 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 12. Left: Paleogeographic map with the location of hominin-related sites in the study area. Numbers correspond to site description in Table 1. Arrows show migration pathways out of Africa and into Europe. Right: Stratigraphy, archaeology and paleomagnetic dating of the oldest hominin findings in Dmanisi, Georgia (from Ferring et al., 2011).
Fig. 5 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 5. Plio-Quaternary time scales for the Pontocaspian domain. GPTS with Systems and Stages are after Hilgen et al. (2012), oxygen isotope curve with numbered Marine Isotope Stages (MIS) is after Lisiecki and Raymo (2005). M=Mammoth (C2An.2r), K=Kaena (C2An.1r), R= Reunion (C2r.1n), O=Olduvai (C2n), C.M.= Cobb Mountain (C1r.2n), J=Jaramillo (C1r.1n). Ages of the Cobb Mountain Subchron are after Channell (2017). On the right side are the time scales for the Caspian Basin, Black Sea Basin and the terrestrial domain (mammal (MN/MQ) zonation and regional (MNR/MQR) biochronological units).
Fig. 2 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 2. Present-day drainage area of the Pontocaspian domain. Yellow circles denote the locations of the stratotype sections of the main Quaternary stages of the Caspian Basin and Black Sea Basin: 1) Akchaghylian on Krasnovodsk peninsula (Turkmenistan), 2) Apsheronian on Apsheron Peninsula (Azerbaijan), 3) Bakunian in Baku (Azerbaijan), 4) Kuyalnikian (Ukraine), 5) Gurian (Georgia), 6) Chaudian on Cape Chauda (Crimea) and 7) Uzunlarian (Crimea). White circles denote the locations of key sections: 1) Pyrnuar (N38.93, E56.26), 2) Malyi Balkhan (N39.27, E54.97), 3) Yuzhny Urundzhik (N39.27, E54.50), 4) Ushak (N40.45, E53.37), 5) Lokbatan (N40.33, E49.75) and Jeirankechmez (N40.24, E47.09), 6) Duzdag (N40.70, E46.92) and Bozdag (N40.80, E46.84), 7) Pantashara (N41.23, E46.36) and 8) Kvabebi (N41.48, E45.68) and Kushkuna (N41.25, E45.44).
Fig. 3. Paleogeographic maps for the Plio-Pleistocene Pontocaspian region. A in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 3. Paleogeographic maps for the Plio-Pleistocene Pontocaspian region. A) Middle Pliocene; B) Late Pliocene; C) Early Pleistocene; D) Middle Pleistocene. Based on Vinogradov, 1961, 1969 and Abdurakhmanov et al. (2002).
Fig. 4 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 4. Distribution of the main ostracod species, foraminifera and charophyta in the Caspian Basin during the Plio-Pleistocene, based on literature data. 1) Cyprideis torosa; 2) Ilyocypris gibba; 3) I. bradyi; 4) Cyprinotus salinus; 5) Eucypris sp.; 6) Pseudocandona compressa (juvenile); 7) Limnocythere aralensis; 8) Zonocypris membranae; 9) Darwinula stevensoni;10) Limnocythere alveolata; 11) L. luculenta; 12) L. tschaplyinae; 13) Typhlocypris gracilis; 14) Loxoconcha eichwaldi (different stages of evolution); 15) Candona candida; 16) C. combibo; 17) Eucythere naphtatscholana; 18a) Amnicythere andrussovi; 18b) A. palimpsesta; 18c) A. normalis; 18d) A. saljanica; 19) Amnicythere nata; 20) Leptocythere gubkini; 21) A.multituberculata; 22) Euxinocythere praebosqueti; 23) A. cymbula; 24) Loxoconcha babazananica; 25) L. petasa; 26) Camptocypria acronasuta; 27) Caspiocypris filona; 28) Tyrrhenocythere bailovi; 29) Xestoleberis chanakovi; 30) T. amnicola donetziensis; 31) T. azerbaidjanica; 32) T. papillosa; 33) Camptocypria acronasuta; 34) Bacunella dorsoarcuata; 35) Loxoconcha gibboides; 36) L. lepida; 37) Euxinocythere bosqueti; 38) Cytherissa bogatschovi; 39) Euxinocythere bacuana; 40) A. quinquetuberculata; 41) Loxoconcha endocarpus
Fig. 16 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 16. Dramatic water level changes in the Pontocaspian domain. Left: Monthly mean Caspian Sea level (CSL) changes observed by tide gauges (1940–1997) and satellite altimetry (1997 to 2015) from Chen et al. (2017). Right: Recent shrinking of the Aral Lake, comparison of satallite images from 1987 (USGS) and 2008 (NASA).
Fig. 15. The 87 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 15. The 87Sr/86Sr and δ18O data from the Black Sea (blue) and Caspian Sea (red) for different time slices (A - D). Different symbols represent different archives; ostracods (boxes), mollusc (triangles), speleothem (circles). The 87Sr/86Sr of the global ocean is obtained from McArthur et al. (2012), for the Black Sea is obtained from Major et al. (2006) and Wegwerth et al. (2014), for the Caspian Sea obtained from Page (2004) and Van Baak (2015). The global benthic δ18O is obtained from Lisiecki and Raymo (2005), for the Black Sea is obtained from Bahr et al. (2006), Major et al. (2006) and the δ18O of the Sofular Cave is obtained from Badertscher et al. (2011).
Fig. 9 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 9. Stratigraphic framework for middle-late Quaternary marine and Pontocaspian intervals in the Marmara Basin and surroundings. The Sofular isotope record is from Badertscher et al. (2011). Blue: Pontocaspian intervals, Red: marine intervals. IL = Iznik Lake. Gateways: (I) Bosphorus, (II) Dardanelles, (III) Sapanca corridor and (IV) Iznik corridor.
Fig. 11 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 11. Dmanisi fauna, selected specimens. Top row from left to right: Homotherium crenatidens, skull, Dm.5/154.A1/87; Megantereon whitei, skull, D1341; Panthera onca georgica, mandible dex., D2027, holotype; Lynx issiodorensis, skull, D3497; Pachycrocuta brevirostris, juvenile mandible dex., Dm.65/ 63.B1x.153; Canis etruscus, skull with mandible, D3420. Second row from left to right: Mammuthus meridionalis taribanensis, articulated hind leg fragment, Dm.64/68.B1x.338; Stephanorhinus etruscus, skull, D3270; Equus stenonis, skull, D353. Third row from left to right: Palaeotragus priasovicus, metatarsus fragment dex., D1757; Cervalces cf. gallicus, antler fragment dex., D1949; Arvernoceros insolitus, antler dex., D2747, holotype; Praemegaceros obscurus, antler fragment dex., D430; Pseudodama nestii, antler sin., D1495. Fourth row from left to right: Bison (Eobison) georgicus, skull fragment, D354, holotype; Gallogoral meneghini sickenbergii, skull, Dm.4/ 154.B1.79; Capra dalii, horn core dex., D75, holotype; Pontoceros surprine, skull fragment D5552, holotype.
Fig. 1 in Quaternary time scales for the Pontocaspian domain: Interbasinal connectivity and faunal evolution
Fig. 1. Ancient map of the Pontocaspian region after Kircher (1678), who in his "Mundus Subterraneus" already envisaged that the Caspian Basin must have been connected to the open ocean to explain its relatively high salinity (> 10 ‰) today. The Caspian Sea is in fact an isolated long-lived lake since at least 2.6 Ma. Kircher considered a subterraneous channel to the Persian Gulf for the marine connection. The location of this marine connection is still enigmatic today.
Deep-time convergent evolution in animal communication presented by shared adaptations for coping with noise in lizards and other animals
<p>Convergence in communication appears rare compared to other forms of adaptation. This is puzzling, given communication is acutely dependent on the environment and expected to converge in form when animals communicate in similar habitats. We uncover deep-time convergence in territorial communication between two groups of tropical lizards separated by over 140 million years of evolution: the Southeast Asian Draco and Caribbean Anolis. These groups have repeatedly converged in multiple aspects of display along common environmental gradients. Robot playbacks to free-ranging lizards confirmed the most prominent convergence in display is adaptive, as it improves signal detection. We then provide evidence from a sample of the literature to further show convergent adaptation among highly divergent animal groups is almost certainly widespread in nature. Signal evolution is therefore curbed towards the same set of adaptive solutions, especially when animals are challenged with the problem of communicating effectively in noisy environments.</p>
Strain Evolution in 2D-Digital Composites-T= 1-15 time steps.
<p>Strain Evolution in 2D - Digital Composites for first 15 time steps.</p> <p>Abaqus simulation script can be found <a href="https://github.com/M3RG-IITD/StrainEvolution">here</a></p>
Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 28. Spinograptus spinosus Wood, 1900, L. nilssoni Biozone, EEP, Poland. 29. Spinograptus latespinosus Kozłowska−Dawidziuk, 1997. 30. Spinograptus munchi Eisenack, 1951, C. praedubeli/C. deubeli Biozone, EEP, Poland. 31. Papiliograptus papilio Lenz and Kozłowska−Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 32. Plectograptus? karlsteinensis Kozłowska−Dawidziuk, Lenz, and Štorch, 2001, C. praedubeli/C. deubeli Biozone, Barrandian. 33. Neogothograptus thorsteinssoni Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 34. Neogothograptus alatiformis Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 35. Neogothograptus purus Kozłowska−Dawidziuk, 1995, EEP, Poland. 36. Holoretiolites mancki (Münch, 1931). 37. Holoretiolites helenaewitoldi sp. nov., L. progenitor Biozone, EEP, Poland. 38. Plectograptus wimani Eisenack, 1951, N. nilssoni Biozone, Baltic erratic boulder, Poland. 39. Plectograptus robustus Obut and Zaslavskaya, 1983, L. nilssoni Biozone, EEP, Kaliningrad. 40. Plectograptus macilentus Törnquist, 1887, L. scanicus Biozone, Baltic erratic boulder, Poland. 41. Semiplectograptus urbaneki Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. 42. Plectodinemagraptus gracilis Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. Figures adapted from: 1, Melchin (1999); 2, 4–5, Bouček and Münch (1944); 3, holotype photographed by A. Lenz; 6, 9, Bates and Kirk (1992); 7, Bates and Kirk (1997); 8, Štorch (1994); 10–15, 38, 40–42, Kozłowska−Dawidziuk (1995); 16, Kozłowska−Dawidziuk (2001); 17–21, Lenz and Kozłowska−Dawidziuk (2001); 22, 25, Kozłowska−Dawidziuk (1990); 23, 35, photo taken by author; 24, 32, Kozłowska−Dawidziuk et al. (2001); 26, 27, 31, Lenz and Kozłowska−Dawidziuk (2002a); 28, 35, photo taken by author; 29, Kozłowska−Dawidziuk (1997); 30, Kozłowska−Dawidziuk 2002; 36, Kozłowska−Dawidziuk and Lenz (2001); 37, this paper; 39, Obut and Zaslavskaya (1983). Not to scale. Abbreviations: RD, Rhuddanian; SHEIN, Sheinwoodian; GORST, Gorstian; LUDF, Ludfordian. Biozonal scheme after Koren' et al. 1996; Geological time scale by International Commission on Stratigraphy, International Union of Geological Sciences 2004 (www.stratigraphy.org) in Evolution of retiolitid graptolites-a synopsis
Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 28. Spinograptus spinosus Wood, 1900, L. nilssoni Biozone, EEP, Poland. 29. Spinograptus latespinosus Kozłowska−Dawidziuk, 1997. 30. Spinograptus munchi Eisenack, 1951, C. praedubeli/C. deubeli Biozone, EEP, Poland. 31. Papiliograptus papilio Lenz and Kozłowska−Dawidziuk, 2002, C. praedubeli/C. deubeli Biozone, Arctic Canada. 32. Plectograptus? karlsteinensis Kozłowska−Dawidziuk, Lenz, and Štorch, 2001, C. praedubeli/C. deubeli Biozone, Barrandian. 33. Neogothograptus thorsteinssoni Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 34. Neogothograptus alatiformis Lenz and Kozłowska−Dawidziuk, 2004, L. progenitor Biozone, Arctic Canada. 35. Neogothograptus purus Kozłowska−Dawidziuk, 1995, EEP, Poland. 36. Holoretiolites mancki (Münch, 1931). 37. Holoretiolites helenaewitoldi sp. nov., L. progenitor Biozone, EEP, Poland. 38. Plectograptus wimani Eisenack, 1951, N. nilssoni Biozone, Baltic erratic boulder, Poland. 39. Plectograptus robustus Obut and Zaslavskaya, 1983, L. nilssoni Biozone, EEP, Kaliningrad. 40. Plectograptus macilentus Törnquist, 1887, L. scanicus Biozone, Baltic erratic boulder, Poland. 41. Semiplectograptus urbaneki Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. 42. Plectodinemagraptus gracilis Kozłowska−Dawidziuk, 1995, Cucullograptus hemiaversus/C. aversus Biozone, EEP, Poland. Figures adapted from: 1, Melchin (1999); 2, 4–5, Bouček and Münch (1944); 3, holotype photographed by A. Lenz; 6, 9, Bates and Kirk (1992); 7, Bates and Kirk (1997); 8, Štorch (1994); 10–15, 38, 40–42, Kozłowska−Dawidziuk (1995); 16, Kozłowska−Dawidziuk (2001); 17–21, Lenz and Kozłowska−Dawidziuk (2001); 22, 25, Kozłowska−Dawidziuk (1990); 23, 35, photo taken by author; 24, 32, Kozłowska−Dawidziuk et al. (2001); 26, 27, 31, Lenz and Kozłowska−Dawidziuk (2002a); 28, 35, photo taken by author; 29, Kozłowska−Dawidziuk (1997); 30, Kozłowska−Dawidziuk 2002; 36, Kozłowska−Dawidziuk and Lenz (2001); 37, this paper; 39, Obut and Zaslavskaya (1983). Not to scale. Abbreviations: RD, Rhuddanian; SHEIN, Sheinwoodian; GORST, Gorstian; LUDF, Ludfordian. Biozonal scheme after Koren' et al. 1996; Geological time scale by International Commission on Stratigraphy, International Union of Geological Sciences 2004 (www.stratigraphy.org)
Deep-time convergent evolution in animal communication presented by shared adaptations for coping with noise in lizards and other animals
Open the record for dataset details and reuse information.
Supplementary information for: The effects of geographic range size and abundance on extinction during a time of 'sluggish' evolution
<p>Geographic range size and abundance are important determinants of extinction risk in fossil and extant taxa. However, the relationship between these variables and extinction risk has not been tested extensively during evolutionarily 'quiescent' times of low extinction and speciation in the fossil record. Here we examine the influence of geographic range size and abundance on extinction risk during the late Paleozoic (Mississippian–Permian), a time of 'sluggish' evolution when global rates of origination and extinction were roughly half those of other Paleozoic intervals. Analyses used spatio-temporal occurrences for 164 brachiopod species from the North American midcontinent. We found abundance to be a better predictor of extinction risk than measures of geographic range size. Moreover, species exhibited reductions in abundance prior to their extinction, but did not display contractions in geographic range size. The weak relationship between geographic range size and extinction in this time and place may reflect the relative preponderance of larger-ranged taxa, combined with the physiographic conditions of the region that allowed for easy habitat tracking that dampened both extinction and speciation. These conditions led to a prolonged period (19 – 25 Myr) during which standard macroevolutionary rules did not apply.</p>
Data from: Eco-evolutionary feedbacks predict the time course of rapid life history evolution
Organisms can change their environment and, in so doing, change the selection they experience and how they evolve. Population density is one potential mediator of such interactions because high population densities can impact the ecosystem and reduce resource availability. At present, such interactions are best known from theory and laboratory experiments. Here we quantify the importance of such interactions in nature by transplanting guppies from a stream where they co-occur with predators into tributaries that previously lacked both guppies and predators. If guppies evolve solely because of the immediate reduction in mortality rate, the strength of selection and rate of evolution should be greatest at the outset then decline as the population adapts to its new environment. If indirect effects caused by the increase in guppy population density in the absence of predation prevail, then there should be a lag in guppy evolution because time is required for them to modify their environment. The duration of this lag is predicted to be associated with the environmental modification caused by guppies. We observed a lag in life history evolution associated with increases in population density and altered ecology. How guppies evolved matched predictions derived from evolutionary theory that incorporates such density effects.
Data from: The early elasmobranch Phoebodus: phylogenetic relationships, ecomorphology, and a new time-scale for shark evolution
Anatomical knowledge of early chondrichthyans and estimates of their phylogeny are improving, but many taxa are still known only from microremains. The nearly cosmopolitan and regionally abundant Devonian genus Phoebodus has long been known solely from isolated teeth and fin spines. Here, we report the first skeletal remains of Phoebodus from the Famennian (Late Devonian) of the Maïder region of Morocco, revealing an anguilliform body, specialized braincase, hyoid arch, elongate jaws and rostrum, complementing its characteristic dentition and ctenacanth fin spines preceding both dorsal fins. Several of these features corroborate a likely close relationship with the Carboniferous species Thrinacodus gracia, and phylogenetic analysis places both taxa securely as members of the elasmobranch stem lineage. Identified as such, phoebodont teeth provide a plausible marker for range extension of the elasmobranchs into the Middle Devonian, thus providing a new minimum date for the origin of the chondrichthyan crown-group. Among pre-Carboniferous jawed vertebrates, the anguilliform body shape of Phoebodus is unprecedented, and its specialized anatomy is, in several respects, most easily compared with the modern frilled shark Chlamydoselachus. These results add greatly to the morphological, and by implication ecological, disparity of the earliest elasmobranchs.
Data used in "Revealing dominant patterns of aerosols regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations" (Li et al., Atmos. Chem. Phys. 2024)
<p>This dataset contains the processed EMAC simulation used as input to the clustering algorithm and the resulting regimes discussed in Li et al. (<em>Atmos. Chem. Phys.</em>, 2024).</p>
Data and Code for "Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution"
<p>This repository contains the datasets and data sources, analysis code, and workflow associated with the manuscript "<em>Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution</em>". The following resources are provided:</p> <ul> <li> <p><strong>Data Files</strong>:</p> <ul> <li><code>time_series_data.csv</code>: A curated time series dataset with dates as rows and NUTS 2 regions as columns. Each column is labeled using a 4-letter abbreviation format "CC.RR", where "CC" represents the country code and "RR" represents the region code. This same abbreviation is also included in the accompanying GeoJSON file.</li> <li><code>geometry_data.geojson</code>: A GeoJSON file representing the spatial boundaries of the NUTS 2 regions, with the same 4-letter abbreviations used in the CSV file. EPSG:4326.</li> <li><code>COVID19_data_sources.xlsx</code>: This Excel file contains important metadata regarding the sources of COVID-19 data used in this study. It includes: <ul> <li>Source of the data for each country</li> <li>Official website(s)</li> <li>The agency responsible for the data</li> <li>Description of the processing steps used to curate the data into the final time series.</li> </ul> </li> </ul> </li> <li> <p><strong>Code</strong>:</p> <ul> <li><code>analysis.py</code>: A Python script used to process and analyze the data. This code can be run using Python 3.x. The libraries required to run this script are listed in the first lines of the code. The code is organized in different numbered sections (1), (2), ... and sub-sections (1a), (1b) ... Make sure to run the script one (sub-)section at a time, so that everything stays overviewable and you don't get all the output at once.</li> </ul> </li> <li> <p><strong>Workflow</strong>:</p> <ul> <li><code>workflow.png</code> : A detailed workflow according to the Knowledge Discovery in Databases (KDD) process, outlining the steps involved in processing and analyzing the data, including the methods used. This workflow provides a comprehensive guide to reproducing the analysis presented in the paper.</li> </ul> </li> </ul>
Monitoring the evolution of relative product populations at early times during a photochemical reaction
<p class="MsoNormal"><span>Identifying multiple rival reaction products and transient species formed during ultrafast photochemical reactions and determining their time-evolving relative populations are key steps towards understanding and predicting photochemical outcomes. Yet, most contemporary ultrafast studies struggle with clearly identifying and quantifying competing molecular structures/species amongst the emerging reaction products. Here, we show that mega-electronvolt ultrafast electron diffraction in combination with <em>ab initio</em> molecular dynamics calculations offers a unique route to determine <em>time-resolved </em>populations of the various isomeric products formed after UV (266 nm) excitation of the five-membered heterocyclic molecule thiophenone. This strategy reveals an unexpectedly high (~50%) yield of an episulfide isomer containing a strained 3-membered ring within ~1 ps at early times and rapid interconversions between the rival photoproducts. </span></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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