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Fig. 21 in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 21. Chaleponcus circumvallatus sp. nov., left gonopod. A. Anterior view. B. Mesal-anterior view. C. Posterior view. D. Mesal-posterior view. al = anterior lobe of telomere, al' = accessory lamella, cu = cucullus, mfp = metaplical flange process, mp = mesal metaplical process, ms = metaplical shelf, mss = metaplical shelf-spine, pl = posterior lobe of telomere, ps = proximal spine of solenomere. Scales 0.1 mm.
Fig. 1 in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 1. Map of the Udzungwa Mountains, showing the location of the forest reserves (green rings) where the material studied here was collected, as well as names of individual mountains in West Kilombero FR. Based on fig. 1 in Marshall et al. (2010).
Fig. 17 in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 17. Chaleponcus mwanihanensis sp. nov., gonopods. A. Left gonopods, mesal-ventral view. B. Left gonopod, posterior view. C. Right gonopod, ventral view. D. Right gonopod, anterior view. al = anterior lamella of telomere, cu = cucullus, lp = lateral coxal process, mf = metaplical flange, ms = metaplical shelf, mss = metaplical shelf-spine, pl = posterior lamella of telomere. Scales 0.1 mm.
Fig. 25 in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 25. Chaleponcus teres sp. nov., gonopods. A. Gonopods in situ in seventh body ring, anterior view. B. Left gonopod, mesal view. C. Right gonopod, base of solenomere and telomere, ventral view. D. Right gonopod, posterior view. E. Left gonopod, anterior(-lateral-ventral) view. F. Right telopodite, mesalanterior-ventral view. G. Right telopodite, mesal-anterior view. al1, al2 = branches of apical telomeral lamella, bal, bal' = basal lamella of telomere, cu = cucullus, in = metaplical incision, mf = metaplical flange, ms = metaplical shelf, mss = metaplical shelf-spine, ps = proximal spine of solenomere, slm = solenomere, spl = spine-like lamella of telomere, st, st' = main stem of telomere. Scales 0.1 mm.
Fig. 20 in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 20. Chaleponcus nectarinia sp. nov., gonopods. A. Right gonopod, posterior (-lateral) view. B. Right gonopod, anterior view. C. Tip of posterior lamella of telomere. D. Left gonopod, mesal (-posterior) view. E. Right gonopod, mesal view. al = anterior lobe of telomere, cu = cucullus, mfp = metaplical flange process, ms = metaplical shelf, mss = metaplical shelf-spine, pl = posterior lobe of telomere, ps = proximal spine of solenomere. Scales 0.1 mm.
Fig. 7 in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 7. Gonopod coxa terminology, left coxa of Chaleponcus termini sp. nov., mesal(-posterior) view. cu = cucullus, mf = anteriad metaplical flange, mfp = metaplical flange process, ms = metaplical shelf, mss = metaplical shelf-spine, prl = proplical lobe.
Fig. 15 in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 15. Chaleponcus scopus sp. nov., right gonopod. A. Posterior view. B. Ventral-posterior view. C. Mesal-posterior view. D. Anterior view. al = anterior lamella of telomere, al' = thumblike process of al, cu = cucullus, lp = lateral coxal process, mf = metaplical flange, mp = mesal metaplical process, msl = metaplical shelf-forming lobe, mss = metaplical shelf-spine, pl = posterior lamella of telomere, ps = proximal solenomeral spine, slm = solenomere. Scales 0.1 mm.
Fig. 8. Gonopod telopodite terminology. — A in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 8. Gonopod telopodite terminology. — A. Left telopodite of Chaleponcus termini sp. nov., posterior view. White oval = basomere, blue oval = torsotope, red oval = telomere, yellow = solenomere (slm), green = proximal spine of solenomere (ps), pxl = proximal lobe of telomere, st = main stem of telomere, st' = shallow lobe of st. B–C. Torsotope (left gonopod) with torsotope process (tp). B. C. vilici sp. nov., mesal-anterior view. C. C. netus sp. nov., posterior view. D. Solenomere (C. dabagaensis) to show longitudinal fluting. E. Tip of solenomere (C. netus sp. nov.) to show spinulation. Scales: 0.1 mm (A–D), 0.01 mm (E).
Fig. 12. Chaleponcus dabagaensis Kraus, 1958 in A mountain of millipedes I: An endemic species-group of the genus Chaleponcus Attems, 1914, from the Udzungwa Mountains, Tanzania (Diplopoda, Spirostreptida, Odontopygidae)
Fig. 12. Chaleponcus dabagaensis Kraus, 1958, left gonopod. — A. Anterior view. B. Posterior view. C. Mesal-ventral view. D. Mesal-dorsal view. al = anterior lamella of telomere, mf = metaplical flange, mp = mesal metaplical process, ms = metaplical shelf, mss = metaplical shelf-spine, pl = posterior lamella of telomere, ps = proximal spine of solenomere, st = main telomeral stem, th = thumblike process of telomere main stem. Scales 0.1 mm.
Figs 202–208 in Depressariidae (Lepidoptera) of the Russian Altai Mountains: new species, new records and updated checklist
Figs 202–208. Habitats of the newly described species, Russia, Altai Mts. 202–203 – Krasnaya Gorka near Chagan Uzun, habitat of Agonopterix kyzyltashensis sp. nov.: 204 – Chulyshman valley, habitat of A. ustjuzhanini sp. nov. and Depressaria paraleucocephala sp. nov.; 205 – Cherga, habitat of A. ustjuzhanini sp. nov.; 206 – steppe near the confluence of Argut and Karagem rivers, habitat of Depressaria paraleucocephala sp. nov.; 207 – rocky steppe near Aktash vill., habitat of A. ustjuzhanini sp. nov.; 208 – mountain steppe near Dzhazator, habitat of A. ustjuzhanini sp. nov.
Figs 106–108 in Depressariidae (Lepidoptera) of the Russian Altai Mountains: new species, new records and updated checklist
Figs 106–108. Male genitalia of Agonopterix ustjuzhanini sp. nov. 106 – holotype, Russia, Altai Mts., details in the text; 107–108 – paratypes, details in the text, valva-complex to show intraspecific variability, especially in shape of cuiller: 107 – Russia, Altai Mts., data as figs 102–105; 108 – Kazakhstan, Kokpek, details in the text; a – aedeagus in lateral view (basal edge of pale area highlighted red); b – aedeagus in ventral view. Scale bar = 1 mm.
Figs 30–34 in Depressariidae (Lepidoptera) of the Russian Altai Mountains: new species, new records and updated checklist
Figs 30–34. Agonopterix anticella (Erschoff, 1877). 30–32 – habitus: 30–31 – Russia, Altai Mts., Chulyshman, 4.–5.vii.2019, J. Šumpich leg. (NMPC); 32 – Russia, Amur region, Radde, without date, ex coll. C. S. Larsen (ZMUC). 33 – male genitalia, Russia, Primorsky krai, Gornotayozhnoe, 3.vi.2015, K. Nupponen & R. Haverinen leg. (RCKN); 34 – female genitalia, data as fig. 32.
Figs 147–148. Depressaria atrostrigella Clarke, 1941 in Depressariidae (Lepidoptera) of the Russian Altai Mountains: new species, new records and updated checklist
Figs 147–148. Depressaria atrostrigella Clarke, 1941, Russia, Altai Republic, Chulyshman valley, 27.–28.vi.2015, J. Šumpich leg. (NMPC): 147 – habitus; 148 – female genitalia.
Fig. 17 in A mountain of millipedes IX: Species of the family Gomphodesmidae from the Udzungwa Mountains, Tanzania (Diplopoda, Polydesmida)
Fig. 17. Emphysemastix frampt Olsen & Enghoff sp. nov., holotype, ♂ (NHMD 621677), left gonopod. A–B. Mesal view. C–D. Lateral view. E–F. Dorsal view. Abbreviations: L = Process L; M = Process M; rl = rounded lobe; sg = subglobose enlargement. Scale bars: 1 mm.
Elevation Models for Reproducible Evaluation of Terrain Representation – Archetypal Landforms – Massanutten Mountain ASCII
<p>An elevation model of Massanutten Mountain, Virginia, USA</p> <p>Landform features: folded ridges, hogback, water gap, meander</p> <p>Resolution: 10 meter, 3,900 x 3,900 height samples</p> <p>File format: Esri ASCII grid</p> <p>This is one model of a set of elevation models: <a href="https://doi.org/10.5281/zenodo.3938020">https://doi.org/10.5281/zenodo.3938020</a>. Please cite the entire set of models.</p> <p>Version 2.0.0 replaced the previous erroneous elevation model of another geographic area.</p> <p>When using this elevation model in an academic publication, please cite the following article, which describes the process and rationale for compiling elevation models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Temperature sensitivity of mountain glaciers
<p><strong>Distributed summer air temperatures across mountain glaciers in the south-east Tibetan Plateau: temperature sensitivity and </strong><strong>comparison with existing glacier datasets</strong></p> <p>Thomas E. Shaw<sup>1</sup>, Wei Yang<sup>2,3</sup>, Álvaro Ayala<sup>4</sup>, Claudio Bravo<sup>5</sup>, Chuanxi Zhao<sup>2</sup>, Francesca Pellicciotti<sup>1,6</sup></p> <p> </p> <p><sup>1</sup> Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland</p> <p><sup>2</sup> Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing, China</p> <p><sup>3</sup> CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China</p> <p><sup>4 </sup>Centre for Advanced Studies in Arid Zones (CEAZA), La Serena, Chile</p> <p><sup>5</sup> School of Geography, University of Leeds, Leeds, UK</p> <p><sup>6 </sup>Department of Geography, Northumbria University, Newcastle, UK</p> <p><em>Corresponding author: Thomas E. Shaw (</em><a href="mailto:thomas.shaw@wsl.ch"><em>thomas.shaw@wsl.ch</em></a><em>)</em></p> <p>Keywords: Air Temperature, Glaciers, Tibetan Plateau, Temperature Sensitivity</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>Dataset provided:</strong></p> <p>'<strong>Climatic_Sensitivity_Mountain_Glaciers.mat'</strong> = Matlab file with data structures for each glacier site. The following sites are:</p> <p>% Parameter set calculated from data on Parlung Glaciers (this study)<br> % Parameter set from Shea and Moore (2010) for Rockies- Canada (Published parameters)<br> % Parameter set from Carturan et al. (2015) for Ortles Cevedale, Italy (k1/k2 data from author)<br> % Parameter set from Shaw et al. (2017) on Tsanteleina Glacier, Italy (Reassesed parameters)<br> % Parameter set calculated from data of Bravo et al., (2019) on South Patagonian Icefield (SPI), Chile<br> % Parameter set calculated from data of Bravo et al., (2017) on Universidad Glacier, Chile<br> % Parameter set calculated from data of Ayala et al., (2015) on Arolla Glacier, Switzerland<br> % Parameter set calculated from data of Ayala et al., (2015) on JuncalNorte Glacier, Chile<br> % Parameter set calculated from data of Troxler et al., (2020) on McCall Glacier, Alaska<br> % Parameter set calculated from data of Greuell and Böhm (1998) on Pasterze Glacier, Austria<br> % Parameter set calculated from data of Rets et al., (2019) on Djankuat Glacier, Russia<br> % Parameter set calculated from data of Pradhananga et al., (2020 In prep) on Peyto Glacier, Canada</p> <p><strong>Variables include:</strong></p> <p>'Name' = name of individual observation station (AWS or Temp/RH 'T-logger')<br> 'Elevation' = Elevation (m a.s.l.) of given observation station<br> 'Flowline' = The distance along the glacier flowline from an upslope summit or crest (m)<br> 'k1' = The climatic sensitivity (ratio) of on-glacier temperatures to changes in the ambient (off-glacier) air temperature below the onset of katabatic onset (following Shea and Moore, 2010)<br> 'k2' = The climatic sensitivity (ratio) of on-glacier temperatures to changes in the ambient (off-glacier) air temperature above the onset of katabatic onset (following Shea and Moore, 2010)<br> 'Tst' = The T* parameter that defines the threshold (off-glacier) temperature for katabatic conditions parameterised following Carturan et al. (2015)<br> 'T1' = The equivalent on-glacier threshold temeprature derived from k1 and Tst<br> 'DataSource' = The citation readout</p> <p><br> <strong>Cited literature</strong><br> Ayala, A., Pellicciotti, F., & Shea, J. (2015). Modeling 2m air temperatures over mountain glaciers: Exploring the influence of katabatic cooling and external warming. Journal of Geophysical Research: Atmospheres, 120, 1–19. https://doi.org/10.1002/2015JD023137.</p> <p><br> Bravo, C., Quincey, D. J., Ross, A. N., Rivera, A., Brock, B. W., Miles, E., & Silva, A. (2019). Air Temperature Characteristics , Distribution , and Impact on Modeled Ablation for the South Patagonia Ice field. Journal of Geophysical Research : Atmospheres, 124, 907–925. https://doi.org/10.1029/2018JD028857</p> <p><br> Bravo, C., Lorlaux, T., Rivera, A., & Brock, B. W. (2017). Assessing glacier melt contribution to streamflow at Universidad Glacier, central Andes of Chile. Hydrology and Earth System Sciences, 21, 3249–3266. https://doi.org/10.5194/hess-21-3249-2017</p> <p><br> Carturan, L., Cazorzi, F., De Blasi, F., & Dalla Fontana, G. (2015). Air temperature variability over three glaciers in the Ortles–Cevedale (Italian Alps): effects of glacier fragmentation, comparison of calculation methods, and impacts on mass balance modeling. The Cryosphere, 9(3), 1129–1146. https://doi.org/10.5194/tc-9-1129-2015</p> <p><br> Greuell, W., & Böhm, R. (1998). 2 m temperatures along melting mid-latitude glaciers , and implications for the sensitivity of the mass balance to variations in temperature. Journal of Glaciology, 44(146), 9–20.</p> <p><br> Rets, E. P., Popovnin, V. V, Toropov, P. A., Smirnov, A. M., Tokarev, I. V, Chizhova, J. N., … Kireeva, M. B. (2019). Djankuat glacier station in the North Caucasus , Russia : a database of glaciological , hydrological , and meteorological observations and stable isotope sampling results during 2007 – 2017. Earth System Science Data, ||, 1463–1481. https://doi.org/https://doi.org/10.5194/essd-11-1463-2019</p> <p><br> Shaw, T. E., Brock, B. W., Ayala, A., Rutter, N., & Pellicciotti, F. (2017). Centreline and cross-glacier air temperature variability on an Alpine glacier: assessing temperature distribution methods and their influence on melt model calculations. Journal of Glaciology, 1–16. https://doi.org/10.1017/jog.2017.65</p> <p><br> Shea, J. M., & Moore, R. D. (2010). Prediction of spatially distributed regional-scale fields of air temperature and vapor pressure over mountain glaciers. Journal of Geophysical Research, 115(D23), D23107. https://doi.org/10.1029/2010JD014351</p> <p><br> Troxler, P., Ayala, Á., Shaw, T. E., Nolan, M., Brock, B. W., & Pellicciotti, F. (2020). Modelling spatial patterns of near-surface air temperature over a decade of melt seasons on McCall Glacier , Alaska. Journal of Glaciology, 1–15. https://doi.org/https://doi.org/10.1017/jog.2020.12</p>
Highlights from 10+ years of lichenological research in Great Smoky Mountains National Park: celebrating the United States National Park Service Centennial
<p>Great Smoky Mountains National Park is renowned as one of the most biologically diverse tracts of land in North America and is the most visited national park in the United States. The park comprises ∼830 square miles, epitomizes eastern temperate hardwood forests of North America, and serves as a refuge for nearly 20,000 documented species from microbes to plants and mammals. Lichens comprise one particularly diverse group of organisms in the park. In this study, we review data from our 11 years of lichenological research in Great Smoky Mountains National Park. Based on approximately 6,000 new field collections generated, the park checklist now includes 920 species, a 129% increase over estimates made two decades ago. Nearly a quarter of the lichens reported in the park are known from only a single occurence whereas only 7% of the lichens are known from 20 or more occurences. An assessment of commonness/rarity for all 920 species indicates that nearly half of the park's lichens should be considered to be infrequent, rare, or exceptionally rare. We assessed the distributions of all 920 species and found that 54 are endemic to the southeastern United States, 30 are endemic to the southern Appalachians, and eight occur nowhere else than within the confines of the national park. We discuss biogeographical affinities of the park's lichen biota as a whole, delimiting six regional "floristic" connections. Our 11 years of research have resulted in the discovery of several species presumed to be extinct or near-extinct. We make one new combination (<em><strong>Fuscopannaria frullaniae</strong></em>) and describe five species as new to science, each commemorating National Park Service staff instrumental to the completion of the study: <em><strong>Heterodermia langdoniana</strong></em>, <em><strong>Lecanora darlingiae</strong></em>, <em><strong>Lecanora sachsiana</strong></em>, <em><strong>Leprocaulon nicholsiae,</strong></em> and <em><strong>Pertusaria superiana</strong></em>.</p>
Figure 2. Summer core area delineation. The straight line with a in Demographic characteristics, seasonal range and habitat topography of Balkan chamois population in its southernmost limit of its distribution (Giona mountain, Greece)
Figure 2. Summer core area delineation. The straight line with a slope of –1 represents the random use of space within the population seasonal range. The curve that sags below the line of random use represents the clumped use of space. The summer core area can be defined at the point whose tangent has slope –1, e.g. 85%, that is, whose tangent is parallel to the line of random use. This is also the point of the curve that is furthest from the line of random use.
Figure 4 in Description of Ghatiana, a new genus of freshwater crab, with two new species and a new species of Gubernatoriana (Crustacea: Decapoda: Brachyura: Gecarcinucidae) from the Western Ghat Mountains, India
Figure 4. Ghatiana hyacintha sp. nov., holotype male (ZSI, WRC-C.1130). (A) Dorsal view; (B) frontal view; (C) ventral view. Scale bars: 10 mm.
Figure 2 in Influence of riparian quality on macroinvertebrate assemblages in subtropical mountain streams
Figure 2. Redundancy analysis (RDA) of macroinvertebrate density in sites of good (white dots), poor (grey dots) and bad (black dots) riparian quality according with the QBRy index. References: DO = dissolved oxygen; Bh = bank-full height; DW = dry channel width; Temp = water temperature; Cond = conductivity; FPOM = fine particulate organic matter.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.