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Figure 6 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 6. Manhattan plots showing SNP levels of ROH per autosome for A, All L. s. svecica individuals, B, Sve_Krk population. The Manhattan plot portrays ROH analysis across 28 autosomes. The height of the peak represents the percentage of individuals sharing homozygous SNP per ROH.
Figure 4 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 4. SNP-based analyses of population structure. A, discriminant analysis of the principal components (DAPC) analysis of genetic structure for two subspecies' genetic clusters (on the left) and, B, for seven populations (on the right). Each colour shade represents subspecies or population genetic clusters, respectively. Every point represents an individual, while inertia ellipses represent 67% of the individuals. Discriminant analysis eigenvalues are displayed by small insets. C, admixture analysis for K = 2. Each vertical bar shows an individual level of shared ancestry between the two subspecies. The two bands below the admixture plot mark individual's subspecies and population affiliation, respectively.
Figure 5 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 5. Pairwise FineRADStructure co-ancestry analysis of 148 genotyped specimens. Ancestral population labels are displayed on the vertical and horizontal axes. Upper horizontal bar stands for subspecies genetic clusters: L. s. svecica—left label, intermediate—centre, L. s. cyanecula—right. Lower horizontal bar depicts population origin if the individuals using the same coding as in Fig. 3.
Figure 3 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 3. The Cytb haplotype network of red-spotted and white-spotted bluethroat populations. Pie charts illustrate the haplotype variants shared among populations. Each circle represents a unique haplotype variant. Sizes of the circles are proportional to the number of individuals. Hatch marks on the branches represent the number of mutational steps that separate haplotypes. Black circles represent hypothetical haplotypes. Red-spotted bluethroat populations are: 1. Krkonoše Mountains (Sve_Krk); 2. Kola (Sve_Klp); 3. Abisko (Sve_Abi). Whitespotted populations are: 1. Třeboňsko (Cya_Trb); 2. St Petersburg (Cya_Stp); 3. Vomáčka (Cya_Vmk); 4. Krkonose Mountains (Cya_Krk). The haplotype marked with the red asterisk is a shared haplotype found in both subspecies.
Figure 1 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 1. Locations of sampled individuals of red-spotted (L. s. svecica) and white-spotted (L. s. cyanecula) bluethroat. L. s. svecica locations: Kola peninsula, Russia (Sve_Klp); Abisko, Sweden (Sve_Abi) and Krkonoše Mountains, Czech Republic (Sve_Krk). L. s. cyanecula locations are: Krkonoše Mountains, Czech Republic (Cya_Krk); Vomáčka, Czech Republic (Cya_Vmk);Třeboňsko, Czech Republic (Cya_Trb) and St. Petersburg, Russia (Cya_Stp). Inset: close-up of the populations in the Czech Republic.
Figure 2 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 2. Box plot comparison of genome-wide heterozygosity by segregating sites at subspecies level (left) and at population level (right). Mean values are marked by a horizontal bar.
FIGURE 3 in Not extinct after all: rediscovery of Commelina dielsii (Commelinaceae) after 140 years, and first record of this species in Uruguay
FIGURE 3. Environment of the population of Commelina dielsii in Paysandú, Uruguay, on the east bank of the Uruguay River. A: view to the north; B: view to the south.
FIGURE 2 in A Bergia in South America: on the neglected, possibly already extinct B. arenarioides (Elatinaceae)
FIGURE 2. Map of the known gatherings of Bergia arenarioides (pink stars). Locations are approximate, due to the imprecision of the available geographic information.
FIGURE 3 in A Bergia in South America: on the neglected, possibly already extinct B. arenarioides (Elatinaceae)
FIGURE 3. Specimen of Bergia arenarioides from Rio Pardo de Minas (D. Vincent 629, L barcode L.2451049). Copyright: Naturalis. Available online at: https://data.biodiversitydata.nl/naturalis/specimen/L.2451049.
FIGURE 1 in A Bergia in South America: on the neglected, possibly already extinct B. arenarioides (Elatinaceae)
FIGURE 1. Lectotype of Merimea arenarioides (A. de Saint-Hilaire B1-1862, P barcode P05490259). Copyright: Muséum National d'Histoire Naturelle. Available online at: https://science.mnhn.fr/institution/mnhn/collection/p/item/p05490259.
Table 4 in Taxonomy, phylogeny, and diversity of the extinct Lesser Antillean rice rats (Sigmodontinae: Oryzomyini), with description of a new genus and species
<p><b>Table 4.</b> Last-occurrence dates for extinct rice rats from the Windward and Leeward Islands, based on historical records or calibrated radiometric dates from archaeological or palaeontological horizons containing rice rat material</p><table><tbody><tr><th></th><th>Archaeological or palaeontological record</th></tr></tbody><tbody><tr><th>Species</th><td>Island</td><td>Historical record</td><td>Site</td><td>Calibrated radiometric date, 2s</td><td>Reference</td></tr><tr><th><i>Megalomys audreyae</i></th><td>Barbuda</td><td></td><td>Darby Sink</td><td>AD 1173–1385</td><td>MacPhee & Flemming (1999)</td></tr><tr><th><i>Megalomys desmarestii</i></th><td>Martinique</td><td><i>c.</i> 1897</td><td></td><td></td><td>Allen (1942)</td></tr><tr><th><i>Megalomys luciae</i></th><td>St. Lucia</td><td>pre-1881</td><td></td><td></td><td>Allen (1942)</td></tr><tr><th><i>Oligoryzomys victus</i></th><td>St. Vincent</td><td>1892</td><td></td><td></td><td>Allen (1942)</td></tr><tr><th><b><i>Pennatomys nivalis</i> gen. et</b> <b>sp. nov.</b></th><td>Nevis</td><td>1720? (1930s?)</td><td>Sulphur Ghaut</td><td>AD 900–1200</td><td>Newsom & Wing (2004); Wilson (2006)</td></tr><tr><td>St. Eustatius</td><td></td><td>Golden Rock</td><td>80 BC–AD 980</td><td>Schinkel (1992)</td></tr><tr><td>St. Kitts</td><td>1631?</td><td>Bloody Point</td><td>AD 660–1115</td><td>J.E. Robb, pers. comm.</td></tr><tr><th>Undescribed taxon</th><td>Anguilla</td><td></td><td>Shoal Bay East</td><td>AD 940–1320</td><td>Crock (2000)</td></tr><tr><th>Undescribed taxon (? <i>Megalomys</i> <i>audreyae</i> or ‘ <i>Ekbletomys</i> <i>hypenemus</i> ’)</th><td>Antigua</td><td></td><td>Indian Creek</td><td>AD 900–1100</td><td>Rouse & Morse (1999)</td></tr><tr><th>Undescribed taxon</th><td>Barbados</td><td>1848?</td><td></td><td></td><td>Schomburgk (1848), Ray (1962), Feilden (1890), Marsh (1984, 1985)</td></tr><tr><th>Undescribed taxon</th><td>Carriacou</td><td></td><td>Grand Bay</td><td>AD 390–1280</td><td>LeFebvre (2007); S.M. Fitzpatrick, pers. comm.</td></tr><tr><td>(Grenadines)</td><td></td><td></td><td></td></tr><tr><th>Undescribed taxon (large morph)</th><td>Grenada</td><td></td><td>Pearls</td><td>37 BC–AD 533</td><td>Haviser (1997)</td></tr><tr><th>Undescribed taxon (small morph)</th><td>Grenada</td><td></td><td>Pearls</td><td>37 BC–AD 533</td><td>Haviser (1997)</td></tr><tr><th>Undescribed taxon</th><td>Guadeloupe</td><td></td><td>Morel</td><td>AD 21–881</td><td>Haviser (1997)</td></tr><tr><th>Undescribed taxon</th><td>La Desirade</td><td></td><td>Petite Rivière</td><td>AD 600–1400</td><td>de Waal (1996)</td></tr><tr><th>Undescribed taxon</th><td>Marie Galante</td><td></td><td>Taliseronde</td><td>AD 350–665</td><td>Haviser (1997)</td></tr><tr><th>Undescribed taxon (large morph)</th><td>Montserrat</td><td></td><td>Trants</td><td>774 BC–AD 622</td><td>Petersen (1996)</td></tr><tr><th>Undescribed taxon (small morph)</th><td>Montserrat</td><td></td><td>Trants</td><td>774 BC–AD 622</td><td>Petersen (1996)</td></tr><tr><th>Undescribed taxon</th><td>Saba</td><td></td><td>Kelbey’s Ridge II</td><td>AD 1290–1400</td><td>Hoogland (1996)</td></tr><tr><th>Undescribed taxon</th><td>St. Martin</td><td></td><td>Hope Estate</td><td>AD 255–650</td><td>Newsom & Wing (2004)</td></tr></tbody></table><p>Calibrated dates were calculated from conventional <sup>14</sup> C ages (years BP) for <i>Megalomys audreyae</i> and for undescribed taxa from Grenada, Guadeloupe, and Marie Galante using OxCal 4.0 (Bronk Ramsey, 1995, 2001). The calibrated date for <i>M</i>. <i>audreyae</i> is based on a direct radiocarbon date from subfossil rice rat material; all other radiometric last-occurrence dates are based on calibrated dates of stratigraphically associated material from archaeological sites.</p>
Data for "The Dust Extinction Curve: Beyond R(V)"
<p>24 million dust extinction curves, detemined from Gaia XP spectra, as described in <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241022537G/abstract">Green, Zhang & Zhang (2025)</a>.</p> <p>We represent the extinction curves using a set of 16 basis vectors. For each star, there are 16 coefficients, which can be used to reconstruct the extinction curves. After loading <code>A_zp</code> and <code>G_subspace</code> from the file <code>G_subspace.json</code>, and <code>coeffs</code> from the file <code>coeffs.h5</code>, the extinction curves can be reconstructed using:</p> <p> <code>A = A_zp + np.sum(coeffs[None,:] * G_subspace[:,:], axis=1)</code></p> <p>The output <code>A</code> will have shape (star, wavelength). The wavelengths at which <code>A</code> is sampled are stored in the field <code>wavelengths_nm</code> (in nanometers), in <code>G_subspace.json</code>.</p> <p>The covariance matrix of the coefficients for each star is stored in the files <code>coeffs_cov_?.h5</code>. We store the diagonals and the upper triangles of the covariances separately. They can be reconstructed using the function <code>reconstruct_symm_matrices</code> from <code>symm_matrix_utils.py</code>:</p> <p> <code>from symm_matrix_utils.py import reconstruct_symm_matrices</code><br> <code>cov = reconstruct_symm_matrices(cov_diag, cov_triu_wo_diag)</code></p> <p>Additionally, we store the inverse covariance matrices in the files <code>coeffs_icov_?.h5</code>, in the same manner as the covariance matrices.</p> <p>The file <code>source_info.h5</code> contains a few useful Gaia fields and parameter estimates (with corresponding uncertainties) from <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240714594Z/abstract">Zhang & Green (2025)</a>.</p> <p>The file <code>feature_EW.h5</code> contains the equivalent widths (in nanometers) of the VBS and the 770 and 850 nm extinction features.</p> <p>Every file contains the Gaia DR3 <code>source_id</code> of every star, labeled <code>gdr3_source_id</code>.</p>
FIGURE 5 in The Okuru skink (Oligosoma carinacauda sp. nov) of South Westland, New Zealand-simply elusive or extinct?
FIGURE 5. (a) Duneland and (b) pakihi wetland habitats adjacent to the type locality—possible habitat types for O. carinacauda sp. nov. Photographs by Tony Jewell.
FIGURES 1. a & b in The Okuru skink (Oligosoma carinacauda sp. nov) of South Westland, New Zealand-simply elusive or extinct?
FIGURES 1. a & b. Oligosoma carinacauda sp. nov., type specimen NMNZ RE005417 as live animal. (a) dorsal and (b) ventral view. Photographs by A.H. Whitaker.
FIGURES 3. a–c in The Okuru skink (Oligosoma carinacauda sp. nov) of South Westland, New Zealand-simply elusive or extinct?
FIGURES 3. a–c. Oligosoma carinacauda sp. nov. type specimen NMNZ RE005417. Photographs by Jean-Claude Stahl (Te Papa Tongarewa / Museum of New Zealand). (a) dorsal; (b) lateral; and (c) ventral views of holotype.
FIGURE 4 in The Okuru skink (Oligosoma carinacauda sp. nov) of South Westland, New Zealand-simply elusive or extinct?
FIGURE 4. Habitat loss at type locality. The type specimen was found in sphagnum in the centre of this photograph. The land has since been drained and converted into pastureland. Photograph by Trent Bell.
Data from: Ecological continuity and transformation after the Permo-Triassic mass extinction in northeastern Panthalassa
The Permo-Triassic mass extinction (PTME) is often implicated in the transition from the Paleozoic Evolutionary Fauna (PEF) to the Modern Evolutionary Fauna (MEF). However the exact timing and details of this progression are unknown, especially regarding the vacating and filling of functional ecological space after the PTME. Here we quantify the functional diversity of middle Permian and Early Triassic marine paleocommunities in the western US to determine functional re-organization in the aftermath of the PTME. Results indicate that while the PTME was selective in nature, many new Triassic taxa either re-filled functional roles of extinct Permian taxa or performed the same functional roles as Permian survivors. Despite this functional overlap, Permian survivors and new Triassic taxa differed significantly in their relative abundances within those overlapping functions. This shift in numerical emphasis, driven by an increase in abundance towards more MEF-style traits, may represent a first step in the transition between the PEF and MEF. We therefore suggest that the extreme impact of the PTME had significant and permanent re-organizational effects on the intrinsic structure of marine ecosystems. Early Triassic ecosystems likely bridged the gap between the Paleozoic and Modern Evolutionary Faunas, as newly originated Triassic taxa shared ecospace with Permian survivors, but shifted functional emphasis.
Data from: A shift in the long-term mode of foraminiferan size evolution caused by the end-Permian mass extinction
Size is among the most important traits of any organism, yet the factors that control its evolution remain poorly understood. In this study, we investigate controls on the evolution of organismal size using a newly compiled database of nearly 25,000 foraminiferan species and subspecies spanning the past 400 million years. We find a transition in the pattern of foraminiferan size evolution from correlation with atmospheric pO2 during the Paleozoic (400-250 Mya) to long-term stasis during the post-Paleozoic (250 Mya to present). Thus, a dramatic shift in the evolutionary mode coincides with the most severe biotic catastrophe of the Phanerozoic (543 Mya to present). Paleozoic tracking of pO2 was confined to Order Fusulinida, whereas Paleozoic lagenides, miliolids, and textulariids were best described by the stasis model. Stasis continued to best describe miliolids and textulariids during post-Paleozoic time, while random walk was the best supported mode for the other diverse orders. The shift in evolutionary dynamics thus appears to have resulted primarily from the selective elimination of fusulinids at the end of the Permian Period. These findings illustrate the potential for mass extinction to alter macroevolutionary dynamics for hundreds of millions of years.
Data from: Correlates of extinction risk in squamate reptiles: the relative importance of biology, geography, threat and range size
Aim Evaluating the relative roles of biological traits and environmental factors that predispose species to an elevated risk of extinction is of fundamental importance to macroecology. Identifying species that possess extinction-promoting traits allows targeted conservation action before precipitous declines occur. Such analyses have been carried out for several vertebrate groups, with the notable exception of reptiles. We identify traits correlating with high extinction risk in squamate reptiles, assess whether these differ with geography, taxonomy and threats, and make recommendations for future Red List assessments. Location Global. Methods We collected data on biological traits and environmental factors for a representative sample of 1139 species of squamate reptiles. We used phylogenetically controlled regression models to identify general correlates of extinction risk, threat-specific correlates of risk and realm-specific correlates of risk. We also assessed the relative importance of range size versus other factors through multiplicative bivariate models, partial regressions and variance partitioning. Results Range size was the most important predictor of extinction risk, reflecting the high frequency of reptiles assessed under range-based IUCN criteria. Habitat specialists occupying accessible ranges were at a greater risk of extinction: although these factors never contributed more than 10% to the variance in extinction risk, they showed significant interactions with range size. The predictive power of our global models ranged from 23% to 29%. The general overall pattern remained the same among geographical, taxonomic and threat-specific data subsets. Main conclusions Proactive conservation requires shortcuts to identify species at high risk of extinction. Regardless of location, squamate reptiles that are range-restricted habitat specialists living in areas highly accessible to humans are likely to become extinct first. Prioritizing species that exhibit such traits could forestall extinction. Integration of data sources on human pressures, such as accessibility of species ranges, may aid robust and time-efficient assessments of species extinction risk.
Data from: Placing cryptic, recently extinct, or hypothesized taxa into an ultrametric phylogeny using continuous character data: A case study with the lizard Anolis roosevelti
In recent years, enormous effort and investment has been put into assembling the tree of life: a phylogenetic history for all species on Earth. Overwhelmingly, this progress toward building an ever increasingly complete phylogeny of living things has been accomplished through sophisticated analysis of molecular data. In the modern genomic age, molecular genetic data have become very easy and inexpensive to obtain for many species. However, some lineages are poorly represented in or absent from tissue collections, or are unavailable for molecular analysis for other reasons such as restrictive biological sample export laws. Other species went extinct recently and are only available in formalin museum preparations or perhaps even as sub-fossils. In this brief communication we present a new method for placing cryptic, recently extinct, or hypothesized taxa into an ultrametric phylogeny of extant taxa using continuous character data. This method is based on a relatively simple modification of an established Maximum Likelihood (ML) method for phylogeny inference from continuous traits. We show that the method works well on simulated trees and data. We then apply it to the case of placing the Culebra Island Giant Anole (Anolis roosevelti) into a phylogeny of Caribbean anoles. Anolis roosevelti is a 'crown-giant' ecomorph anole hypothesized to have once been found throughout the Spanish, U.S., and British Virgin Islands, but that has not been encountered or collected since the 1930s. Although this species is widely thought to be closely related to the Puerto Rican giant anole, A. cuvieri, our ML method actually places A. roosevelti in a different part of the tree and closely related to a clade of morphologically similar species. We are unable, however, to reject a phylogenetic position for A. roosevelti that places it as sister taxon to A. cuvieri; although close relationship with the remainder of Puerto Rican anole species is strongly rejected by our method.
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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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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.