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Fig. 3. Prodasineura Cowley, 1934 spp., males. A–B, E. P in A revision of the systematics and distribution of the damselfly genus Prodasineura Cowley, 1934 (Odonata: Zygoptera: Platycnemididae) in Vietnam with description of two new species
Fig. 3. Prodasineura Cowley, 1934 spp., males. A–B, E. P. coerulescens (Fraser, 1932), ZCDTU 2016100801-ODO. C–D, F. P. doisuthepensis Hoess, 2007, ZCDTU 2017062201-ODO. G–I. P. hoffmanni Kosterin, 2015, ZCDTU 2017062202-ODO. A, C, G = tip of abdomen, dorsal view; B, D, H = tip of abdomen, lateral view. E–F, I: Genital ligula, oblique-ventral view. Images not to scale.
Fig. 2. Prodasineura Colwey, 1934 in A revision of the systematics and distribution of the damselfly genus Prodasineura Cowley, 1934 (Odonata: Zygoptera: Platycnemididae) in Vietnam with description of two new species
Fig. 2. Prodasineura Colwey, 1934 spp., head and thorax, lateral view (A, C, E: ♂, B, D, F: ♀) A–B. P. coerulescens (Fraser, 1932), ZCDTU 2016100801-ODO. C–D. P. doisuthepensis Hoess, 2007, ZCDTU 2017062201-ODO. E–F. P. hoffmanni Kosterin, 2015, ZCDTU 2017062202-ODO (E). Fig. 2F is modified from Kosterin (2015: fig. 2a), from Dak Dam village, Mondulkiri Province, Cambodia. Images not to scale.
Fig. 2 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 2. Linear relationship (solid line) and 95 % confidence interval (gray area) between habitat quality predicted by the BART model (x-axis) and shell height (H in millimeters, y-axis), derived from the linear mixed model.
Fig. 4. Partial dependence plot for topographic Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 4. Partial dependence plot for topographic Fig. 5. Partial dependence plot for terrain roughness wetness index (TWI). index (tri).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o). Fig. 7. Partial dependence plot for silt content (SLT).
Habitats as predictors in species distribution models: Shall we use continuous or binary data?
<p>The representation of a land cover type (i.e., habitat) within an area is often used as an explanatory variable in species distribution models. However, it is possible that a simple binary presence/absence of the suitable habitat might be the most important determinant of the presence/absence of some species and, thus, be a better predictor of species occurrence than the continuous parameter (area). We hypothesize that the binary predictor is more suitable for relatively rare habitats (e.g., wetlands) while for common habitats (e.g., forests) the amount of the focal habitat is a better predictor. We used the Third Atlas of Breeding Birds in the Czech Republic as the source of species distribution data and CORINE Land Cover inventory as the source of the landcover information. To test our hypothesis, we fitted generalized linear models of 32 water and 32 forest bird species. Our results show that for water bird species, models using binary predictors (presence/absence of the habitat) performed better than models with continuous predictors (i.e., the amount of the habitat); for forest species, however, we observed the opposite. Thus, future studies using habitats as predictors of species occurrences should consider the prevalence of the habitat in the landscape, and the biological role of the habitat type in the particular species' life history. In addition, performing a preliminary comparison of the performance of the binary and continuous versions of habitat predictors (e.g., using information criteria) prior to modelling, during variable selection, can be beneficial. These are simple steps that will improve explanatory and predictive performance of models of species distributions in biogeography, community ecology, macroecology, and ecological conservation.</p>
Fig. 1. The Carpathian Exeristes spp. distribution map. Yellow circle — E in A Review Of The Genus Exeristes (Hymenoptera, Ichneumonidae, Pimplinae) From Carpathians, With An Illustrated Key To Western Palearctic Species
Fig. 1. The Carpathian Exeristes spp. distribution map. Yellow circle — E. roborator; red circle — E. longiseta; green circle — E. arundinis.
Fig. 3. Partial dependence plot for BIO17 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 3. Partial dependence plot for BIO17 = Precipitation of Driest Quarter; gray area = 95 % confidence interval.
Fig. 6 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 6. Result of the analysis of Binomial tests (CliMond 2090 (2081–2100)): A — T. graeca; B — T. hermanni.
Fig. 3 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 3. Niche clustering (Geographic space, CliMond 1975 (1970–2000)) from: A — T. graeca (1. T. g. ibera, 2. T. nikolskii, 3. T. g. anamurensis, 4. T. g. floweri, 5. T. g. antakyensis, 6. T. g. pallasi, 7. T. g. armenica, 8. T. g. perses, buxtoni, 9. T. g. terrestris); B — T. hermanni (1. T. h. hermanni, 2. T. h. hervegovinensis, 3. T. h. boettgeri), red circles showing the approximate ranges of subspecies according to "Turtles…, 2017" World" (2017).
Fig. 2 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 2. The "Ecological envelope" — relationship bio01 "Annual mean temperature", °C & bio12 "Annual precipitation", mm (DivaGis): A — T. graeca; B — T. hermanni.
Fig. 5 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 5. Potential (probabilistic) model of T. hermanni world expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).
Fig. 4 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 4. Potential (probabilistic) model of T. graeca expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021 a). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).
Fig. 4 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 4. Distribution of wetlands number depending on number of species in them (axis X — number of species, axis Y — number of wetlands).
Fig. 5 in The Amount And Distribution Of The Red Data Book Bird Wetland Species In The Azov-Black Sea Region Of Ukraine According To The Results Of August Counts 2004-2015
Fig. 5. Distribution of wetlands depending on species number (axis X) and average amount of birds in them (axis Y).
Fig. 1–4 in Redescription And New Data On Distribution Of Glyphomerus Flavabdomen With Key To The Palearctic Species Of The Genus Glyphomerus (Hymenoptera, Torymidae)
Fig. 1–4: Glyphomerus flavabdomen Zerova: 1 — female, lateral view; 2 — abdomen and ovipositor; 3 — fore wing venation; 4 — antenna.
Fig. 1 in Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)
Fig. 1. Potential distribution of the Common vole Microtus arvalis. White circles are georeferenced occurrences of genetically identified individuals; black indicates areas of maximum habitat suitability, white are areas of lowest suitability.
Fig. 1 in Species Composition And Distribution Of Oribatids (Acari, Oribatei) In Urbanized Biotopes Of Kyiv
Fig. 1. Cluster analisys of oribatid species composition in urbanized biotopes of Kyiv, Ukraine (groups 1—16 are given in Results and discussion).
Fig. 5. The potential distribution map for F in Distribution Of The Freshwater Snail Species Fagotia (Gastropoda, Melanopsidae) In Ukraine According To Climatic Factors. I. Fagotia Esperi
Fig. 5. The potential distribution map for F. esperi in Ukraine under climatic conditions projected for 2050. Captions as in fig. 4, Bu — "Southern Buh".
Fig. 4. The potential distribution map for F in Distribution Of The Freshwater Snail Species Fagotia (Gastropoda, Melanopsidae) In Ukraine According To Climatic Factors. I. Fagotia Esperi
Fig. 4. The potential distribution map for F. esperi in Ukraine under contemporary climatic conditions (black squares represent pixels of 10-minute resolution, predicted to be suitable for the species). Convex polygons are drawn around assumed clusters: N — "northern", Ds — "Dnister", Du — "Danube", Dn — "Dnipro".
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.