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Figs 3–4 in New data on distribution of Decticus nigrescens Tarbinsky, 1930 (Orthoptera: Tettigoniidae) in Russia
Figs 3–4. Predicted probabilities of suitable conditions for Decticus nigrescens. 3 – according the Maxent model (all distribution data and bioclimatic variables for 1970–2000; point-wise mean for 25 replicates); 4 – according the ellipsoid envelope model (all distribution data and selected bioclimatic variables for 1970–2000; means for 25 replicates).
Fig. 5 in New data on distribution of Decticus nigrescens Tarbinsky, 1930 (Orthoptera: Tettigoniidae) in Russia
Fig. 5. Reliability test for the Decticus nigrescens Maxent distribution model (bioclimatic variables for 1970–2000; 25 replicates with cross-validation).
Fig. 1 in New data on distribution of Decticus nigrescens Tarbinsky, 1930 (Orthoptera: Tettigoniidae) in Russia
Fig. 1. Decticus nigrescens female from the Lazovsky Nature Reserve, Primorsky Krai (Photo S. Storozhenko).
Figs 6–10 in Taxonomy and distribution of the flea beetle Altica ivlievi L.Medvedev, 1968 (Coleoptera: Chrysomelidae: Galerucinae: Alticini)
Figs 6–10. Altica ivlievi, female: 6 – imago, general view; 7 – left mandible; 8 – spermatheca; 9 – styles; 10 – tignum. (Sikhote-Alin Reserve, 18.IV 2018).
Fig. 11 in Taxonomy and distribution of the flea beetle Altica ivlievi L.Medvedev, 1968 (Coleoptera: Chrysomelidae: Galerucinae: Alticini)
Fig. 11. Feeding and copulating beetles of Altica ivlievi on the leaves of Betula platyphylla Sukacz. (Sikhote-Alin Reserve, 11.V 2018).
Figure 1 in Fauna and distribution of house dust mites in two northern districts of Kerala, India
Figure 1. The distribution of mite Dermatophagoides pteronyssinus in rural and urban areas of Malappuram and Kozhikkode districts.
Figure 3. Critical stop lines for a sequential count plan for T. urticae. For a in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 3. Critical stop lines for a sequential count plan for T. urticae. For a precision level of 10 and 25%.
Figure 2 in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 2. Sample sizes required to achieve a given precision level of 10 and 25% at different mean densities of T. urticae per leaf.
Figure 1 in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops
Figure 1. Relationship between variance and mean density (all stages combined per leaf) of T. urticae samples collected from bean fields near Varamin vicinity, Tehran province, Iran. The red lines are the best-fitting lines of Taylor's power law.
Figures 1-3 in Redescription of Agrilus (Agrilus) piuraensis Juárez & González, 2017 (Coleoptera: Buprestidae: Agrilinae), with notes on its ecology and distribution in Peru
Figures 1-3. Agrilus (Agrilus) piuraensis Juárez & González, 2017. 1a-b. Female, dorsal and ventral view. 2a–b. Male, dorsal and ventral view. Scale: 5 mm. 3. Aedeagus, ventral view. Scale: 1 mm. / 1ab. Hembra, vista dorsal y ventral. 2a-b. Macho, vista dorsal y ventral. Escala: 5 mm. 3. Edeago, vista ventral. Escala: 1 mm.
Figures 6-7 in Redescription of Agrilus (Agrilus) piuraensis Juárez & González, 2017 (Coleoptera: Buprestidae: Agrilinae), with notes on its ecology and distribution in Peru
Figures 6-7. Agrilus (Agrilus) piuraensis Juárez & González, 2017. 6. Feeding on leaves of Senna sp. 7. Copulating on leaves of Senna sp. / 6. Alimentándose sobre hojas de Senna sp. 7. Copulando sobre hojas de Senna sp.
Figures 4-5 in Redescription of Agrilus (Agrilus) piuraensis Juárez & González, 2017 (Coleoptera: Buprestidae: Agrilinae), with notes on its ecology and distribution in Peru
Figures 4-5. Agrilus (Agrilus) piuraensis Juárez & González, 2017. 4a-b. Male, dorsal view and labels. (BMNH). Scale: 5 mm. Photographs by Keita Matsumoto (BMNH). 5a-b. Holotype, dorsal view and labels. (MUPRG). Scale: 5 mm. / 4a-b. Macho, vista dorsal y etiquetas. (BMNH). Escala: 5 mm. FotografÍas por Keita Matsumoto (BMNH). 5a-b. Holotipo, vista dorsal y etiquetas (MUPRG). Escala: 5 mm.
Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling
<p>A necessary component of understanding vector-borne disease risk is the accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data-rich species but may produce inaccurate results for sparsely-documented vectors. In light of global change, vectors that are currently not well-documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a<strong> </strong>sparsely-documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely-documented species. To demonstrate this approach, we evaluated the ability of twelve models—which differed in their pooling of data from multiple vector species and inclusion of disease data—to improve distribution estimates of sparsely-documented vectors. We assessed our models on two simulated data sets, which allowed us to generalize our results and examine their mechanisms. We found that when the focal species is sparsely documented, incorporating data on related vector species reduces uncertainty and improves accuracy by reducing overfitting. When data on vector species are already incorporated, disease data only marginally improve model performance. However, when data on other vectors are not available, disease data can improve model accuracy and reduce overfitting and uncertainty. We then assessed the approach on empirical data on ticks and tick-borne diseases in Florida and found that incorporating data on other vector species improved model performance. This study illustrates the value of exploiting correlated data via joint modeling to improve distribution models of data-limited species.</p>
Figure 3. A in Redescription and distribution of Acrida indica Dirsh, 1954 (Orthoptera: Acrididae)
Figure 3. A. Male supra-anal plate, B. Male subgenital plate, C. Epiphallus, D. Aedeagus, E. Female supra-anal plate, F. Female subgenital plate, G. Spermatheca, H. Ovipositor.
Figure 2 in Redescription and distribution of Acrida indica Dirsh, 1954 (Orthoptera: Acrididae)
Figure 2: A. Dorsal view of head and pronotum of male, B. Frontal ridge of male, C. Ventral view of sternum of male, D. Lateral view of head and pronotum of male, E. Antenna of male, F. Dorsal view of male abdominal apex, G. Ventral view of male abdominal apex, H. Dorsal view of hind knee lobe of male, I. Lateral view of hind knee lobe of male, J. Dorsal view of apex of hind tibia, K. Dorsal view of hind arolium of male, L. Lateral view of male abdominal apex, M. Dorsal view of female abdominal apex, N. Ventral view of female abdominal apex, O. Lateral view of female abdominal apex.
FIGURE 5 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil
FIGURE 5 | The proportion of body depth at dorsal-fin origin (% SL) versus the standard length (mm) in females (red triangles) and males (blue dots) in Characidium onca. Symbols indicating the mature females are highlighted in the dashed area.
FIGURE 6 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil
FIGURE 6 | Maps of the tributaries of rio Paranaíba in Goiás State, Brazil in general view (left), with the rio São Bartolomeu basin detailed in the Distrito Federal (right). The type locality of Characidium onca is indicated by a star in the córrego Taquara. Abbreviations: BA, Bahia; DF, Distrito Federal; GO, Goiás; MG, Minas Gerais; MS, Mato Grosso do Sul; MT, Mato Grosso; and TO, Tocantins.
FIGURE 4 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil
FIGURE 4 | Pseudotympanum in Characidium onca (MZUSP 125801, paratype). Abbreviations: 5th pl, pleural rib of fifth vertebra; a, anterior window of pseudotympanum; lln, lateral line nerve; ls, lateralis superficialis; oi, obliquus inferioris; os, obliquus superioris; p, posterior window of pseudotympanum. Scale bar = 1 mm.
FIGURE 7 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil
FIGURE 7 | Satellite images of the Area de Preservação Ambiental das Bacias do Gama e Cabeça de Veado (highlighted in red), showing the land use and cover changes along the past 34 years, and predicted: in A. year of 1986, with urban areas concentrated north of the FAL–UNB, RECOR and EEJBB; B. year of 1996, illustrating the beginning of expansion of urban and rural areas just east of the FAL–UNB, RECOR and EEJBB; C. year of 2006, after the construction of the Juscelino Kubitschek bridge in 2002, illustrating a rapid expansion of urban areas in the eastern area; and D., predicted situation according to the Distrito Federal Territorial Planning Master Plan (2009, 2019). Green polygons indicate protected areas; yellow, urban areas; blue, rural areas; black, public land; and, red arrow, the Juscelino Kubitschek bridge. Abbreviations: FAL–UNB, Estação Experimental Fazenda Águas Limpas of the University of Brasília; RECOR, Reserva Ecológica do Instituo Brasileiro de Geografia e Estatística; and EEJBB, Estação Ecológica do Jardim Botânico de Brasília. Source of maps: Google Earth.
FIGURE 1 in A new, narrowly distributed, and critically endangered species of Characidium (Characiformes: Crenuchidae) from the Distrito Federal, Central Brazil
FIGURE 1 | Characidium onca, holotype, MZUSP 125807, 40.1 mm SL, male. A. Specimen in lateral view soon after collection; B–D. Preserved specimen in lateral dorsal, ventral views.
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.