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2,441 results for “extinct”
Fig. 1 in New Extinct Carp Fish Species (Teleostei, Cyprinidae) From The Late Neogene Of Southeastern Europe
Fig. 1. Type localities: Odesa Pontian Lectostratotype (Ukraine), Priozernoe (Republic of Moldova).
Fig. 5 in Further Data On The Extinct Ant Genus Eocenomyrma (Hymenoptera, Formicidae)
Fig. 5. Eocenomyrma ukrainica sp. n., photo of paratype gyne, body in lateral view. Scale bar 1 mm.
Figure 4 from: Costa WJEM (2019) Description of a new species of cynopoeciline killifish (Cyprinodontiformes, Aplocheilidae), possibly extinct, from the Atlantic Forest of south-eastern Brazil. ZooKeys 867: 73-85. https://doi.org/10.3897/zookeys.867.34034
Figure 4 Habitat of L.sanguineus sp. nov. in 1988.
Figure 1 from: Costa WJEM (2019) Description of a new species of cynopoeciline killifish (Cyprinodontiformes, Aplocheilidae), possibly extinct, from the Atlantic Forest of south-eastern Brazil. ZooKeys 867: 73-85. https://doi.org/10.3897/zookeys.867.34034
Figure 1 Leptopanchaxsanguineus sp. nov., MNRJ 51331, holotype, male, 20.9 mm SL. Scale bar: 5 mm.
Figure 16. BMNH r2197 in Morphology, relationships, and biogeographical significance of an extinct horned crocodile (Crocodylia, Crocodylidae) from the Quaternary of Madagascar
Figure 16. BMNH r2197, osteoderms from dorsal shield. Scale = 1 cm.
Figure 1 from: Gibson G (2013) The extinct Baltic amber genus Propelma Trjapitzin, a valid genus of Neanastatinae (Hymenoptera, Eupelmidae). ZooKeys 283: 59-69. https://doi.org/10.3897/zookeys.283.4665
Figure 1 - Propelma rhodendorfi, amber block bearing holotype female.
Smith et al 2023 Extinct in the Wild data
<p>Dataset containing information on the conservation history and current status of all species known to have been ex situ restricted or Extinct in the Wild between 1950 and 2022.</p>
Morphological trends across the Norian/Rhaetian boundary within Late Triassic conodonts in western Canada: Implications for protracted paleoenvironmental disturbance preceding the end-Triassic mass extinction
<p>Appendix for "Morphological trends across the Norian/Rhaetian boundary within Late Triassic conodonts in western Canada: Implications for protracted paleoenvironmental disturbance preceding the end-Triassic mass extinction".</p>
Data from: Island biodiversity in peril: anticipating a loss of mammals' functional diversity with future species extinctions
<p>Islands are biodiversity hotspots that host unique assemblages. However, a substantial proportion of island species are threatened and their long-term survival is uncertain. Identifying and preserving vulnerable species has become a priority, but it is also essential to combine this information with other facets of biodiversity like functional diversity, to understand how future extinctions might affect ecosystem stability and functioning. Focusing on mammals, we (i) assessed how much functional space would be lost if threatened species go extinct, (ii) determined the minimum number of extinctions which would cause a significant functional loss, (iii) identified the characteristics (e.g., biotic, climatic, geographic, or orographic) of the islands most vulnerable to future changes in the functional space, and (iv) quantified how much of that potential functional loss would be offset by introduced species. Using trait information for 1,474 mammal species occurring in 318 islands worldwide, we built trait probability density functions to quantify changes in functional richness and functional redundancy in each island if the mammals categorized by IUCN as threatened disappeared. We found that the extinction of threatened mammals would reduce the functional space in 63% of the assessed islands, although these extinctions in general would cause a reduction of less than 15% of their overall functional space. Also, on most islands, the extinction of just a few species would be sufficient to cause a significant loss of functional diversity. The potential functional loss would be higher on small, isolated and/or species rich islands and, in general, the functional space lost would not be offset by introduced species. Our results show that the preservation of native species and their ecological roles remains crucial for maintaining the current functioning of island ecosystems. Therefore, conservation measures considering functional diversity are imperative to safeguard the unique functional roles of threatened mammal species on islands.</p> <p><strong>Datasets and R scripts provided: </strong></p> <p><strong>1. Mammals_occurrence_islands.xlxs</strong> - Matrix of presence/absence of mammals species (columns) per island (rows) and bibliographic sources from which the information has been extracted. Islands are grouped according to the zoogeographical regions proposed by Holt el al. (2013).</p> <p><strong>2. Traits_matrix.csv</strong> – Matrix of species functional traits included in this study and the bibliographic sources of this information. Note that trait values are scaled and centered and that some of them have been imputed (see Methods section of the original manuscript).</p> <p><strong>3.</strong> <strong>Functional_space_analysis.R</strong> – script to:</p> <p>· Build the functional space of islands</p> <p>· Calculate observed functional richness and functional redundancy</p> <p>· Calculate functional richness and functional redundancy after simulating the extinction of threatened species</p> <p>· Calculate the functional space offset by introduced species </p> <p><strong>4. mixed_models_islands.R</strong> - R script to perform the linear mixed models to explore whether islands with higher values of predicted functional diversity loss due to threatened species extinction share some characteristics.</p> <p><strong>5. data_models_islands.csv </strong>- Dataset used to perform the models. For each island the following information is provided:</p> <ul> <ul> <li>Island ID (ID)</li> <li>Number of species (SppRich)</li> <li>Number of threatened species (Thre_sp)</li> <li>Functional richness (Island_FRic)</li> <li>Functional redundancy (Island_Red)</li> <li>Functional richness standard effect size (SES_FRic)</li> <li>Functional redundancy standard effect size (SES_FRed)</li> <li>Island group (Archipielago)</li> <li>Island past connectivity (Type)</li> <li>Percentage of protected area coverage (protected_percentageI_VI)</li> <li>Distance to the nearest continent (dContinent_km)</li> <li>Mean annual temperature (Anntemp_promedio)</li> <li>Mean annual precipitation (Annprec_promedio)</li> <li>Island area (Area_km2)</li> <li>Maximum elevation (Elev_max)</li> <li>Species richness (SR)</li> <li>Mean human footprint (Human_foot)</li> <li>Distance to the nearest larger landmass (distance_biggerLandmass)</li> </ul> </ul> <p>Island area, distance to the nearest continent and distance to the nearest larger landmass were calculated with ArcMap (ESRI, 2019) and the ‘terra 1.7-71’ R package (Hijmans, 2023), using the shapefile of the world’s islands available in Martin et al. (2022). Distance to the nearest mainland and to the nearest larger landmass were calculated as the shortest distance between coastlines (Weigelt & Kreft, 2013). Maximum elevation of each island was extracted from the Global Bathymetry and Elevation Database (Becker et al., 2009). We also used this database to access the bathymetry around the continents and islands and determine whether an island was connected to the mainland during the Last Glacial Maximum (about 20,000 years ago), assuming a sea level of 122 m below the present level (glacial maximum mainland connection; Weigelt, Jetz and Kreft, 2013). Averaged values of annual temperature and annual precipitation for each island were calculated using the climatic variables available in the CHELSA 2.1 database (Karger et al., 2018) at a resolution of 30 arc seconds. To calculate the percentage of protected area on each island, we gathered the protected surface’s shapefile from The World Database on Protected Areas (UNEP-WCMC & IUCN, 2022). Finally, we used the mean human footprint index from Human Footprint maps (see Venter et al., 2018).</p> <p><strong>References</strong></p> <p>Becker, J. J., Sandwell, D. T., Smith, W. H. F., Braud, J., Binder, B., Depner, J., Fabre, D., Factor, J., Ingalls, S., Kim, S.-H., Ladner, R., Marks, K., Nelson, S., Pharaoh, A., Trimmer, R., Von Rosenberg, J., Wallace, G., & Weatherall, P. (2009). Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. <em>Marine Geodesy</em>, 32(4), 355–371. <a href="https://doi.org/10.1080/01490410903297766">https://doi.org/10.1080/01490410903297766</a></p> <p>ESRI (2019). <em>ArcGis for Desktop</em>. Retrieved from https://desktop.arcgis.com/en/</p> <p>Hijmans, R. (2023). terra: Spatial Data Analysis_. R package version 1.7-3, <https://CRAN.R-project.org/package=terra>.</p> <p>Holt, B. G., Lessard, J.-P., Borregaard, M. K., Fritz, S. A., Araújo, M. B., Dimitrov, D., Fabre, P.-H., Graham, C. H., Graves, G. R., Jønsson, K. A., Nogués-Bravo, D., Wang, Z., Whittaker, R. J., Fjeldså, J., & Rahbek, C. (2013). An update of Wallace’s zoogeographic regions of the world. <em>Science</em>, 339(6115), 74–78. https://doi.org/10.1126/science.1228282</p> <p>Karger D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2018). Data from: Climatologies at high resolution for the earth's land surface areas [Dataset]. <em>Dryad</em>. https://doi.org/10.5061/dryad.kd1d4</p> <p>Martin, M., Sayre, R., VanGraafeiland, K., McDermott Long, O., Weatherdon, L., Will, D., Spatz, D. R., & Holmes, N. D. (2020). Global Islands (M. I. Goldstein & D. A. B. T.-E. of the W. B. DellaSala (eds.); pp. 47–50). Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.12475-3</p> <p>UNEP-WCMC & IUCN (2022). <em>Protected Planet: The World Database on Protected Areas (WDPA).</em> Cambridge, UK: UNEP-WCMC and IUCN. Retrieved from <a href="http://www.protectedplanet.net">www.protectedplanet.net</a>. [Accessed 12/2022]</p> <p>Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J., Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete, B. M., Levy, M. A., & Watson, J. E. (2018). <em>Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release</em>. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). <a href="https://doi.org/10.7927/H46T0JQ4">https://doi.org/10.7927/H46T0JQ4</a></p> <p>Weigelt, P., & Kreft, H. (2013). Quantifying island isolation – insights from global patterns of insular plant species richness. <em>Ecography,</em> 36(4), 417-429. https://doi.org/10.1111/j.1600-0587.2012.07669.</p> <p>Weigelt, P., Jetz, W., & Kreft, H. (2013). Bioclimatic and physical characterization of the world’s islands. <em>Proceedings of the National Academy of Sciences</em>, 110(38), 15307–15312. https://doi.org/10.1073/pnas.1306309110</p> <p> </p>
Neuromodulation of the Fear Extinction Circuit Using Temporally and Anatomically Specific TMS in Humans
ClinicalTrials.gov study NCT05368987. IPD Sharing: YES. Countries: 1. Publications: 0.
Facilitation of Fear Extinction With Yohimbine Hydrochloride in Phobic Participants
ClinicalTrials.gov study NCT00734422. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Extinction Based Treatment for Nicotine Dependence
ClinicalTrials.gov study NCT00831155. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Influence of Valproic Acid on Extinction-based Therapy in Patients With Fear of Spiders.
ClinicalTrials.gov study NCT02789813. IPD Sharing: NO. Countries: 1. Publications: 0.
Social Support and Enhanced Fear Extinction
ClinicalTrials.gov study NCT04565041. IPD Sharing: NO. Countries: 1. Publications: 0.
Cerebellar Neuromodulation to Enhance Fear Extinction and Predict Response to Exposure Therapy
ClinicalTrials.gov study NCT03275337. IPD Sharing: NO. Countries: 1. Publications: 0.
Opioids and Social Support Enhanced Extinction Effects
ClinicalTrials.gov study NCT04166071. IPD Sharing: NO. Countries: 1. Publications: 0.
Estradiol and Fear Extinction in Anorexia Nervosa (AN)
ClinicalTrials.gov study NCT02792153. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Influence of Aerobic Exercise on Consolidation of Fear Extinction Learning in PTSD
ClinicalTrials.gov study NCT06144203. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Facilitation of Extinction Retention and Reconsolidation Blockade by IV Allopregnanolone in PTSD.
ClinicalTrials.gov study NCT07079761. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effects of Treatment of PTSD on Reduced Recall for Fear Extinction
ClinicalTrials.gov study NCT01228253. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
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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.