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Fig. 6. Kelleria andamanensis Sewell, 1949, male. A in Copepods of the family Kelleriidae (Crustacea, Copepoda, Cyclopoida) from tropical waters of the Asia-Pacific
Fig. 6. Kelleria andamanensis Sewell, 1949, male. A, habitus, dorsal; B, urosome, ventral; C, maxilla; D, maxilliped; E, leg 1; F, endopod of leg 2; G, exopod of leg 5; H, leg 6. Scale bars: A, 0.2 mm; B, 0.1 mm; C-F, H, 0.05 mm; G, 0.02 mm.
Fig. 11. Kelleria robusta n in Copepods of the family Kelleriidae (Crustacea, Copepoda, Cyclopoida) from tropical waters of the Asia-Pacific
Fig. 11. Kelleria robusta n. sp., female. A, habitus, dorsal; B, urosome, dorsal; C, caudal rami, dorsal; D, rostrum; E, antennule; F, antenna; G, labrum; H, mandible; I, maxillule; J, maxilla. Scale bars: A, 0.5 mm; B-G, 0.1 mm; H-J, 0.05 mm.
Fig. 4. Kelleria andamanensis Sewell, 1949, female. A in Copepods of the family Kelleriidae (Crustacea, Copepoda, Cyclopoida) from tropical waters of the Asia-Pacific
Fig. 4. Kelleria andamanensis Sewell, 1949, female. A, habitus, dorsal; B, urosome, dorsal; C, caudal rami, dorsal; D, egg sac; E, rostrum; F, antennule; G, antenna; H, labrum; I, mandible; J, maxillule; K, maxilla. Scale bars: A, B, D, 0.1 mm; C, E-H, K, 0.05 mm; I, J, 0.02 mm.
Fig. 13. Kelleriella quadridens n. gen. n in Copepods of the family Kelleriidae (Crustacea, Copepoda, Cyclopoida) from tropical waters of the Asia-Pacific
Fig. 13. Kelleriella quadridens n. gen. n. sp., female. A, habitus, dorsal; B, urosome, dorsal; C, right caudal ramus, dorsal; D, rostrum; E, antennule; F, antenna; G, distal part of antenna; H, labrum; I, mandible. Scale bars: A, 0.2 mm; B, E, 0.1 mm; C, G, I, 0.02 mm; D, F, H, 0.05 mm.
FIGURE 14. A-G in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 14. A-G. Amamriabatis heni nov gen. nov. sp. A. anterior tooth KEB 1-218, A1. Occlusal view, A2. Basal view, A3. Lingual view, B. antero-lateral tooth KEB 1-219 (Holotype), B1. Occlusal view, B2. Lingual view, B3. Basal view, C. juvenile tooth KEB 1-220, occlusal view, D. antero-lateral tooth KEB 1-221, D1. Occlusal view, D2. Basal view, D3. Labial view, E. lateral tooth KEB 1-222, E1. Occlusal view, E2. Lingual view, E3. Basal view, F. antero-lateral tooth KEB 1-223, F1. Occlusal view, F2. Basal view, F3. Labial view, G. lateral tooth KEB 1-224, G1. Occlusal view, G2. Basal view, G3. Lingual view; H. Archaeomanta sp. KEB 1-225, H1. Lateral view, H2. Labial view.
FIGURE 12. A-C in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 12. A-C. Mecotrygon asperodentulus nov. gen nov. sp. A. anterior tooth KEB 1-188, A1. occlusal view, A2. Profile, A3. Basal view; B. lateral tooth KEB 1-189, B1. occlusal view, B2. labial view, B3. Profile; C. lateral tooth KEB 1-190, HOLOTYPE, C1. Occlusal view, C2. Lingual view, C3. Basal view, C4. Profile; D-M. Himantura souarfortuna nov. sp. D.?posterior tooth KEB 1-191, D1. Occlusal view, D2. Profile, D3. Basal view; E. antero-lateral tooth KEB 1- 192, occlusal view; F. anterior tooth KEB 1-193, F1. Occlusal view, F2. Labial view; G. antero-lateral tooth KEB 1-194, G1. Occlusal view, G2. Profile; H. anterior tooth KEB 1-195, occlusal view; I. lateral tooth KEB 1-196, occlusal view; J. A. anterior tooth KEB 1-197, occlusal view; K. lateral tooth KEB 1-198, K1. Occlusal view, K2. Basal view; L. A. anterior tooth KEB 1-199, occlusal view; M. lateral tooth KEB 1-200, occlusal view. N-O. Dasyatoid indet. N. antero-lateral tooth KEB 1-201, N1. Occlusal view, N2. Basal view; O. A. antero-lateral tooth KEB 1-202, occlusal view; P-Q. Arechia sp. P. lateral tooth KEB 1-203, occlusal view; Q. A. anterior tooth KEB 1-204, Q1. Occlusal view, Q2. Lingual view.
FIGURE 10. A-D in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 10. A-D: Propristis cf. schweinfurti. A. Rostral denticle KEB 1-172, A1. Profile, A2. dorsal view; B. Rostral denticle KEB 1-173, Profile; C. Rostral denticle KEB 1-174, C1. Profile, C2. Dorsal view, C3. basal view; D. Rostral denticle KEB 1-175, D1. profile. D2. dorsal view; E: Pristis sp. Rostral denticle, KEB 1-165, dorsal view; F-G. Rhynchobatus cf. vincenti. F. anterior tooth KEB 1-176, F1. Occlusal view, F2. Basal view; G. anterior tooth KEB 1-177, occlusal view; H-I.?Torpedo sp. H. lateral tooth KEB 1-178, occlusal view, I. lateral tooth KEB 1-179, occlusal view.
FIGURE 9. A-F in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 9. A-F: Propristis cf. schweinfurti, A. Anterior oral tooth KEB 1-166, A1. Lingual view, A2. Occlusal view, A3. profile; B. Antero-lateral oral tooth KEB 1-167, B1. Occlusal view, B2. Lingual view, B3. Labial view; C. Anterior oral tooth KEB 1-168, C1. Lingual view, C2. Basal view, C3. Profile; D. lateral tooth KEB 1-169, D1. Lingual view, D2. Occlusal view, D3. Basal view, D4. Magnificence of crown-root boundary of D3; E.?male lateral tooth KEB 1-170, E1. Occlusal tooth, E2. Basal view; F.?male anterior tooth KEB 1-171, F1. Lingual view, F2. Occlusal view; G-L: Pristis sp. G. porterior tooth KEB 1-158, G1. Occlusal view, G2. Basal view; H. anterior tooth KEB 1-159, occlusal view; I. lateral tooth KEB 1-160, I1 occlusal view, I2., lingual view. J lateral tooth KEB 1-161, occlusal view; K. anterior tooth KEB 1-162, occlusal view; L. lateral tooth of?juvenile KEB 1-163, L1. Occlusal view, L2. Basal view; M. lateral tooth of juvenile KEB 1-164, M1. Occlusal view, M2. Profile.
FIGURE 1 in Diversity and renewal of tropical elasmobranchs around the Middle Eocene Climatic Optimum (MECO) in North Africa: New data from the lagoonal deposits of Djebel el Kébar, Central Tunisia
FIGURE 1. Paleotemperatures (ice-free deep-ocean T°/ tropical sea surface T°) and Thermic events during the "doubthouse" conditions of Eocene period (from Cramwinckel et al., 2018 modified with events dating from Hollis et al., 2019). Stratigraphically and geographical locations of the main deposits with Elasmobranch associations along the southwestern Tethys. Abbreviations: DAK: Dakhla (Adnet et al., 2010), GEN: Genam (Zouhri et al., 2017, in press); AZ: Aznag (Tabuce et al., 2005) PM: Phosphate ores (see Noubhani and Cappetta, 1997), Morocco; GAF: Gafsa basin (see Arambourg, 1952); KEBAR: Kébar (this work and Adnet et al., 2019); MBK: Mabrouk (see Sweydan et al., 2019), Tunisia; EG: ElGedida (see Strougo et al., 2007); KM: KM11 (see Adnet et al., 2011) MT: Minqar Tabaghbagh (see Zalmout et al., 2012); BQ: Birquet Qarun QS: Quar et Sa; GE: Genahamm Fm.; MI: Midawara FM. from Wadi al Hitan, see Underwood et al., 2011), Egypt; QD: Qa Faydat al Dahikya, Jordania, see Mustafat and Zalmout, 2002).
WV-TTL: water vapor mixing ratio from GEOSCCM and trajectory model simulations in tropical tropopause layer
<p>This dataset includes 100 hPa water vapor mixing ratio simulated from a trajectory transport model and a climate-chemistry model in the tropical tropopause layer from 2005 to 2016 in the format of netCDF. The data are monthly and have three dimensions as lon/lat/time in the unit of parts per million by volume.</p> <p>Also included the tropical average time series of indices for Brewer-Bobson circulation (BDC), tropospheric temperature and/or Quasi-biennial Oscillation (QBO) from ERAi/MERRA-2/GEOSCCM. These indices are used in a multivariate regression.</p>
Data for "Sounding out Ecoacoustic Metrics: Avian species richness is predicted by acoustic indices in temperate but not tropical habitats"
<p>This deposit contains the data for the paper <strong>A Multi-habitat, Comparative Evaluation of Ecoacoustic Indices for Biodiversity Monitoring: Acoustic Indices Predict Avian Species Richness in Temperate but not Tropical Habitats. (Ecological Indicators) </strong>The dataset contains a series of 1 min wav files recorded across UK and Ecuadorian habitats. Each one has 26 acoustic indices calculated on it, and a full list of avian species and abundances and GPS data for each sample site.</p> <p>Abstract</p> <p>Affordable, autonomous recording devices facilitate large scale acoustic monitoring and Rapid Acoustic Survey is emerging as a cost-effective approach to ecological monitoring; the success of the approach rests on the development of computational methods by which biodiversity metrics can be automatically derived from remotely collected audio data. Dozens of indices have been proposed to date, but systematic validation against classical, in situ diversity measures. This study conducted the most comprehensive comparative evaluation to date of the relationship between avian species diversity and a suite of acoustic indices across a wide range of ecological conditions. Acoustic surveys were carried out across habitat gradients in temperate and tropical biomes. Baseline avian species richness and subjective multi-taxa biophonic density estimates were established through aural counting by expert ornithologists. 26 acoustic indices were calculated and compared to observed variations in species diversity. Five acoustic diversity indices (Bioacoustic Index, Acoustic Diversity Index, Acoustic Evenness Index, Acoustic Entropy, and the Normalised Difference Sound Index) were assessed as well as three simple acoustic descriptors (root-mean-square, spectral centroid and zero-crossing rate). Highly significant correlations, of up to 65%, between acoustic indices and avian species richness were observed across temperate habitats, supporting the use of automated acoustic indices in biodiversity monitoring where a single vocal taxon dominates. Significant, weaker correlations were observed in neotropical habitats which host multiple non-avian vocalizing species. Multivariate classification analyses suggest that AIs also track observed differences in habitat-dependent community composition and that each habitat has a distinct soundscape. Multivariate analyses of the relative predictive power of AIs show that compound indices are more powerful predictors of avian species richness than any single index and simple descriptors contribute to predicting avian diversity in multi-taxa tropical environments. Our results support the use of community level acoustic indices as a proxy for species richness and point to the potential for tracking of habitat-dependent changes in community composition. Recommendations for the design of compound indices for multi-taxa community composition appraisal are put forward, with consideration for the requirements of next generation, low power remote monitoring networks.</p> <p> </p> <p><strong>Sampling Methods (extract from paper)</strong></p> <p>Acoustic surveys were carried out along a gradient of habitat degradation (1 forested, 2 regenerating forest and 3 agricultural land) in South East (SE) England and North Western (NW) Ecuador. The six sites (UK1, UK2, UK3, EC1, EC2, EC3) were sampled consecutively from May 6th - Aug 25th 2015.</p> <p>All UK sites were in the county of Sussex, in SE England, an area of weald clays (Fig. 2, left) and included ancient woodland (UK1), regenerating farmland with patches of woodland (UK2) and a downland barley farm (UK3).1 min mono audio recordings made every 15 minutes at three different habitats in the UK</p> <p>Ten day acoustic surveys were carried out consecutively at each study site using 15 Wildlife Acoustics Song Meter audio field recorders. Sampling points were arranged in a grid at a minimum distance of 200 m to minimise pseudo replication (the sound of most species being attenuated over this distance in all biomes). Altitudinal range of sample points across sites was minimised in order to prevent introduction of extraneous, confounding gradients (UK varied between 10 m – 50 m and Ecuador 130 m – 390 m). Recording schedules captured 1 min every 15 min around the clock for 10 days at each site, resulting in 960 recordings at each of 15 sample points for 3 habitat types in 2 different climates (86,400 1 minute recordings in total). Data across the 15 sample points was pooled; inter-site variation was not explored in the current analyses. In the UK 3½ hours of each dawn chorus was sampled starting at 1 hour before sunrise. This range was determined to capture the onset, progression and peak of the dawn chorus, creating a temporal gradient. The equatorial dawn chorus is more compact and was sampled for 2¼ hours starting 15 mins before sunrise, capturing a comparable chorus onset and peak.</p> <p> </p> <p> </p>
Determinants of Reef Fish Assemblages on Tropical Oceanic Islands
<p><strong>ABSTRACT</strong></p> <p>Diversity patterns are determined by biogeographic, energetic, and anthropogenic factors, yet few studies have combined them into a large-scale framework in order to decouple and compare their relative effects on fish faunas. Using an empirical dataset derived from 1527 underwater visual censuses (UVC) at 18 oceanic islands (five different marine provinces), we determined the relative influence of such factors on reef fish species richness, functional dispersion, density and biomass estimated from each UVC unit. Species richness presented low variation but was high at large island sites. High functional dispersion, density, and biomass were found at islands with large local species pool and distance from nearest reef. Primary productivity positively affected fish richness, density and biomass confirming that more productive areas support larger populations, and higher biomass and richness on oceanic islands. Islands densely populated by humans had lower fish species richness and biomass reflecting anthropogenic effects. Species richness, functional dispersion, and biomass were positively related to distance from the mainland. Overall, species richness and fish density were mainly influenced by biogeographical and energetic factors, whereas functional dispersion and biomass were strongly influenced by anthropogenic factors. Our results extend previous hypotheses for different assemblage metrics estimated from empirical data and confirm the negative impact of humans on fish assemblages, highlighting the need for conservation of oceanic islands.</p> <p><strong><em>Keywords:</em></strong> species richness, functional dispersion, density, fish biomass, biogeographic factors, energetic factors, anthropogenic factors, marine provinces<em>.</em></p>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
FIG. 2 in Two new species and a new combination in the genus Pauridiantha Hook.f. (Rubiaceae) from tropical Africa
FIG. 2. — Distribution map of Pauridiantha chlorantha (K.Schum.) Ntore & O.Lachenaud, comb. nov.
FIG. 1 in Two new species and a new combination in the genus Pauridiantha Hook.f. (Rubiaceae) from tropical Africa
FIG. 1. — Seeds of Pauridiantha chlorantha (K.Schum.) Ntore & O.Lachenaud, comb. nov., from Jannerup & Mhoro 129 (A, B) and P. gracilipes O.Lachenaud & Ntore, sp. nov., from Lachenaud et al. 1981 (C, D) in electron microscopy: A, C, entire seed; B, detail of exotesta cells with outer tangential wall removed, showing thickened radial walls with small pits, and large pits in the inner tangential walls; D, detail of exotesta cells with outer tangential wall removed, showing thin radial walls with small pits, and large pits in the inner tangential walls. Scale bars: A, C, 100 µm; B, D, 10 µm.
Fig. 8 in Empidoid flies from Cabo Verde (Diptera, Empidoidea, Dolichopodidae and Hybotidae) are not only composed of Old World tropical species
Fig. 8. Crossopalpus complicatus sp. nov., male holotype, terminalia (leg. P. Grootaert and I. Van de Velde, RBINS). A. Right surstyli. B. Lateral view of right epandrial lamella. C. Dorsal view of epandrium, with detail of left surstyli. D. Tip of right surstylus from inside. Scales: 0.1 mm.
Fig. 1 in Empidoid flies from Cabo Verde (Diptera, Empidoidea, Dolichopodidae and Hybotidae) are not only composed of Old World tropical species
Fig. 1. Aphrosylus salensis sp. nov., male holotype, habitus (leg. P. Grootaert and I. Van de Velde, RBINS).
Fig. 2 in Empidoid flies from Cabo Verde (Diptera, Empidoidea, Dolichopodidae and Hybotidae) are not only composed of Old World tropical species
Fig. 2. Aphrosylus salensis sp. nov., male holotype, terminalia. A. Epandrium, lateral view. B. Cerci, dorsal view. C. Ventral surstylus, lateral view. D. Ventral view of epandrium, below the epandrial lobes. E. Ventral view of epandrium. Scales: 0.1 mm.
Fig. 5 in Empidoid flies from Cabo Verde (Diptera, Empidoidea, Dolichopodidae and Hybotidae) are not only composed of Old World tropical species
Fig. 5. Crossopalpus salensis sp. nov., male paratype, terminalia (leg. P. Grootaert and I. Van de Velde, RBINS). A. Right epandrial lamella with right surstylus. B. Dorsal view of epandrium. C. Tip of right surstylus, with left surstyli below. D. Left surstyli, lateral view. Scales: 0.1 mm.
Fig. 7 in Empidoid flies from Cabo Verde (Diptera, Empidoidea, Dolichopodidae and Hybotidae) are not only composed of Old World tropical species
Fig. 7. Crossopalpus complicatus sp. nov., male holotype, habitus (leg. P. Grootaert and I. Van de Velde, RBINS).
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.