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Figure 1 in Species identification and seasonal prevalence of house dust mites in Assiut City, Egypt: A descriptive study in an urban area
Figure 1. Dermatophagoides farinae (adult female) – a. Habitus (before being cleared) (10×); b. Habitus (after being cleared in Hoyer's medium) (x100); c. Distal solenidion on tarsus I (arrow head), terminal spinous process (curved arrow) and tarsus II with the two distal solenidia (arrow) (200×); d. Magnified tarsus II with distal solenidia (arrow head) and two small spinous tubercles (long arrow) (400×); e. The low-arched epigynium (arrow) and the faint transverse striations above it (arrow head); f. Bursa copulatrix (arrow), its external opening and sclerotized part (arrow head).
Figure 4 in Species identification and seasonal prevalence of house dust mites in Assiut City, Egypt: A descriptive study in an urban area
Figure 4. Dermatophagoides farinae adult male (SEM photo) – a. Ventral view showing the enlarged first pair of legs. B. The aedeagus; c. The anal plate containing the anal suckers.
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Рис.1. Карта-схема района исследований. ● – станции отбора планктонных и бентосных проб. Fig.1. A schematic map of the studied area. ● – sampling stations. in Species composition and distribution of bivalve mollusks in plankton and benthos in Nevelsky Strait in summer
Рис.1. Карта-схема района исследований. ● – станции отбора планктонных и бентосных проб. Fig.1. A schematic map of the studied area. ● – sampling stations.
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye. in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye.
Рис. 1. Карта иЗученного побереЖьЯ Северной Кореи (пров. Хамгён-пукто) с укаЗанием населенных пунктов Чипсам и ЁмбудЖин и видами берегов: а, b, c – побереЖье вблиЗи Чипсам; d, e, f – побереЖье вблиЗи ЁмбудЖин. Fig. 2. A map of the area studied along the coast of North Korea (North Hamgyong Province) with indication of two sites, Jipsam and Yombunjin, and coastline views: а, b, c – coasts of Jipsam; d, e, f – coasts of Yombunjin. in On the bivalve molluscan fauna of North Hamgyong Province (North Korea)
Рис. 1. Карта иЗученного побереЖьЯ Северной Кореи (пров. Хамгён-пукто) с укаЗанием населенных пунктов Чипсам и ЁмбудЖин и видами берегов: а, b, c – побереЖье вблиЗи Чипсам; d, e, f – побереЖье вблиЗи ЁмбудЖин. Fig. 2. A map of the area studied along the coast of North Korea (North Hamgyong Province) with indication of two sites, Jipsam and Yombunjin, and coastline views: а, b, c – coasts of Jipsam; d, e, f – coasts of Yombunjin.
Рис. 1. Места отбора проб телеуправлЯемым подводным аппаратом в Охотском море. Fig. 1. The map of the studied area in the Sea of Okhotsk. in Rare and interesting deep-sea finds of the buccinid gastropods (Gastropoda: Buccinidae) from the Sea of Okhotsk
Рис. 1. Места отбора проб телеуправлЯемым подводным аппаратом в Охотском море. Fig. 1. The map of the studied area in the Sea of Okhotsk.
Fig. 1. Study area and surrounding intertidal habitats. A in Two new species of Perinereis Kinberg, 1865 (Annelida: Nereididae) from the rocky shore of Maharashtra, India, including notes and an identification key to Group 1
Fig. 1. Study area and surrounding intertidal habitats. A. Coastal region of Maharashtra (western India) and collecting localities. B. Aerial view of the rocky intertidal habitat of the study area. C. Intertidal area with rocks covered with seaweed. D. Close-up view of the intertidal area with rocks covered with seaweed and oyster shells. E. Scraped-out seabed (oyster shells, algae, and sediment), burrows of polychaetes and other small invertebrates can be seen. F. Close-up view of burrowing nereidid dwelling among the seabed. Red arrows point to burrowing worms. Scale bars: B = 20 m; C = 1 m; D = 50 cm; E–F = 3 cm.
Improving the application of Important Plant Areas to conserve threatened habitats: a case study of Uganda
<p><strong>This data set relates to the publication: Richards, S. L., Kalema, J., Ojelel, S., Williams, J., & Darbyshire, I. (2024). Improving the application of Important Plant Areas to conserve threatened habitats: A case study of Uganda. Conservation Science and Practice, e13246. https://doi.org/10.1111/csp2.13246<br></strong></p> <p><strong>Abstract:</strong></p> <p>Important Plant Areas (IPAs) are a successful method of identifying priority areas for plant conservation. Assessment of IPAs, however, often relies on criteria related to species, while incorporation of habitats has been less consistent. Using Uganda as a case study, we test the application of the threatened habitat criterion – criterion C. We identified nationally threatened habitats using Red List of Ecosystems criteria and assess, for the first time, how differing application of thresholds under IPA criterion C can influence IPA network outcomes. Eleven threatened habitats were identified, with declines switching from predominantly forest to savanna after the mid-20<sup>th</sup> century. Significantly, we found current IPA guidance on use of criterion C needlessly limits the number of sites that qualify as IPAs. The “five best sites” IPA threshold is reserved for countries where quantitative data is unavailable, however, the application of the relevant numerical thresholds (site contains ≥10% of national resource or site is among the best quality examples required to collectively prioritisie up to 20% of the national resource) to quantitative data largely generated fewer than five IPAs, comparably limiting conservation opportunities identified. We recommend, therefore, that the “five best” threshold is available for application on both qualitative and quantitative data. This will bolster the value of IPAs in conserving and restoring threatened and ecologically important habitats under the Kunming-Montreal Global Biodiversity Framework.</p> <p><strong>Dataset:</strong></p> <p>Within this dataset is a shapefile of the estimated extent of threatened habitats in Uganda. Each polygon represents a single "site" for each threatened habitat, with methodology for site identification given in the manuscript. Feature area and percentage national resource are given for each site, enabling users to identify those that trigger the different IPA criterion C thresholds.</p> <p><strong>In this study, we have preliminarily identified the threatened habitats and IPAs for Uganda. However, it is important to seek the expertise and views of stakeholders, consider other IPA criteria met and any complementarity between sites when identifying IPAs. In addition, ground-truthing or more localised data could validate the threat status of a vegetation type as well as identifying which sites are best to conserve these habitats. </strong></p>
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
Fig. 5 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 5: Size-class (S: small, M: medium and L: large) frequency distribution (%) of relevant target fishes in Unprotected (UP) and Future Protected (FP) zones at the three studied locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka), (Number of individuals used to calculate percentages is given in Supp. Mat. 2).
Fig. 3 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 3: Mean density (±standard error) per trophic category at the sampling locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka) and per protection level (UP: Unprotected, FP: Future Protected).
Fig. 1 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 1: Locations where MPAs will be established along the Tunisian coast. Location of future protected sites (FP) and those outside (that will remain unprotected) (UP) (separated with dotted lines indicating borders of future MPAs as they are proposed in management plans).
Fig. 4 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 4: Mean biomass (±standard error) per trophic category at the sampling locations (KU: Kuriat Islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka) and per protection level (UP: Unprotected, FP: Future Protected).
Fig. 2 in Fish assemblages along the coasts of Tunisia: a baseline study to assess the effectiveness of future Marine Protected Areas
Fig. 2: Mean species richness (a), mean density (b) and mean biomass (c) (±standard error) per location (KU: Kuriat islands, CNCS: Cap Negro-Cap Serrat, TA: Tabarka) and protection level (UP: Unprotected, FP: Future Protected).
Fig. 2 in Heracleum Sosnowskyi Manden. Monitoring In Protected Areas - A Case Study In Rēzekne Municipality, Latvia
Fig. 2. Two studied sites in Rāzna National Park in Rēzekne municipality, Mākoņkalna village, Jegorova. Polygon No. 14 - the largest increase of Heracleum sosnowskyi cover from 2012 to 2016 among the studied sites, Polygon No. 15 – the largest new site found in 2016 (Table 2). Yellow stripes - H. sosnowskyi cover in 2012, purple colour – H. sosnowskyi cover in 2016 (Author: Elīna Tripāne).
Fig. 6. A and C in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 6. A and C: Batch fecundity as a function of total length and total weight (without ovary), respectively. B and D: relative fecundity as a function of total length and total weight (without ovary), respectively.
Fig. 8 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 8. Proportion of mature individuals observed for each length classes of Anchoa marinii. A. Females, B. Males.
Fig. 7 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 7. Monthly variation of the gonadosomatic index (GSI), based on an annual cycle. Boxplots with median, 75th percentile and 25th percentile. Bars denote standard deviation. Open circles= outlier values; asterisk= extreme outliers.
Fig. 4 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 4. Stages of oocyte development of Anchoa marinii. A, oogonias (o) and primary growth (p) oocytes; B, cortical alveoli stage oocyte (arrow); C, yolked oocytes (arrow); D, details of a yolked oocyte (r: radiata zone; g: granulosa cells; t: teca cells); E, migration of the nucleus (n); F, hydrated oocytes; G, atretic follicle; H, post- ovulatory follicle (arrow). Scale bars: A, B, D, 20 µm; C, E, F, G, H, 70 µm.
ScienceDex guides
Understand access before you commit
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.