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Figure 3 in Taxonomy, distribution, and ecology of crustacean zooplankton in trough waters of Ankara (Turkey)
Figure 3. Distribution of the numbers of species among 142 troughs from no species (0) in 43 troughs to 5 (5+) or more species in 3 troughs.
Fig. 4. – A in Mimusops coriacea (A. DC.) Miq. (Sapotaceae): nomenclature, distribution and ecology
Fig. 4. – A tree of Mimusops coriacea (A. DC.) Miq. preserved in the city of Masoala, Eastern Madagascar. [Photo: L. Gautier]
Fig. 2 in Mimusops coriacea (A. DC.) Miq. (Sapotaceae): nomenclature, distribution and ecology
Fig. 2. – Natural range of Mimusops coriacea (A. DC.) Miq. in Madagascar, plotted on HUMBERT (1955) map of phytogeographical domains (102 specimens georeferenced with <5 km incertainity).
Fig. 3 in Mimusops coriacea (A. DC.) Miq. (Sapotaceae): nomenclature, distribution and ecology
Fig. 3. – Phenology of Mimusops coriacea (A. DC.) Miq. in Madagascar (number of specimens having flower buds, flowers, young fruits or fruits per month)
Fig. 1 in Mimusops coriacea (A. DC.) Miq. (Sapotaceae): nomenclature, distribution and ecology
Fig. 1. – Mimusops coriacea (A. DC.) Miq. A. In fruit near Masoala, Eastern Madagascar; B. Flower in Orangea, ntsiranana. [Photo: A: L. Gautier; B: R. Randrianaivo]
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
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. 4. Смешанный лес в месте обитаниЯ Eostrobilops coreana в северной части п-ова Песчаный. Фото Л.А. ПроЗоровой, 1.11.2020 г. in On the distribution and ecology of a rare land snail, Eostrobilops coreana (Pilsbry, 1927) (Gastropoda: Pulmonata: Strobilopsidae)
Рис. 4. Смешанный лес в месте обитаниЯ Eostrobilops coreana в северной части п-ова Песчаный. Фото Л.А. ПроЗоровой, 1.11.2020 г.
Рис. 2. ИЗвестные местообитаниЯ улитки Eostrobilops coreana: 1 – СевернаЯ КореЯ, окрестности ПхеньЯна; 2 – СевернаЯ КореЯ, Канвондо, хребет Кымгансан; 3 – ЮЖнаЯ КореЯ, Чхунчхон-Пукто, окрестности г. ТанЯн; 4 – ЮЖнаЯ КореЯ, Канвондо, окрестности д. Синчервон; 5 – Приморский край, Заповедник КедроваЯ падь; 6 – Приморский край, хребет ЛоЗовый, пеЩера МедвеЖий Клык; 7 – Приморский край, п-ов Песчаный. Условные обоЗначениЯ: Зеленый цвет – леса раЗличной плотности, красный – урбаниЗированные территории, Желтый – сельскохоЗЯйственные Земли (по: Jakub [2018]). in On the distribution and ecology of a rare land snail, Eostrobilops coreana (Pilsbry, 1927) (Gastropoda: Pulmonata: Strobilopsidae)
Рис. 2. ИЗвестные местообитаниЯ улитки Eostrobilops coreana: 1 – СевернаЯ КореЯ, окрестности ПхеньЯна; 2 – СевернаЯ КореЯ, Канвондо, хребет Кымгансан; 3 – ЮЖнаЯ КореЯ, Чхунчхон-Пукто, окрестности г. ТанЯн; 4 – ЮЖнаЯ КореЯ, Канвондо, окрестности д. Синчервон; 5 – Приморский край, Заповедник КедроваЯ падь; 6 – Приморский край, хребет ЛоЗовый, пеЩера МедвеЖий Клык; 7 – Приморский край, п-ов Песчаный. Условные обоЗначениЯ: Зеленый цвет – леса раЗличной плотности, красный – урбаниЗированные территории, Желтый – сельскохоЗЯйственные Земли (по: Jakub [2018]).
Рис. 1. ЭкоΛого-географическая характеристика зоопΛанктона гиΔротермаΛьной зоны оз. Кенон в июΛе 2019 г.: А — зоогеография, Б — местообитание, В — способ переΔвижения, Г — способ питания Fig. 1. Ecological and geographic characteristics of zooplankton in the hydrothermal zone of Lake Kenon in July 2019: А — zoogeography, Б — habitat, В — type of locomotion, Г — type of feeding in Zooplankton Structure And Distribution In The Hydrothermal Zone Of Cooling Reservoirs (Trans-Baikal Territory)
Рис. 1. ЭкоΛого-географическая характеристика зоопΛанктона гиΔротермаΛьной зоны оз. Кенон в июΛе 2019 г.: А — зоогеография, Б — местообитание, В — способ переΔвижения, Г — способ питания Fig. 1. Ecological and geographic characteristics of zooplankton in the hydrothermal zone of Lake Kenon in July 2019: А — zoogeography, Б — habitat, В — type of locomotion, Г — type of feeding
Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range
<p>Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.</p>
Fig.3 in Biodiversity survey, ecology and new distribution records of Marchantiophyta in a remnant of Brazilian Atlantic Forest
Fig.3. Graphical representation of substrates colonized by liverwort species in the fragment of dense montane ombrophilous forest studied in the National Park of Boa Nova, Bahia, Brazil.
Figure 1 in New data on pond snails (Mollusca: Gastropoda: Lymnaeidae) inhabiting the Ukrainian Transcarpathian: diversity, distribution and ecology
Figure 1. Map showing the localities of samples studied. Details for each sampling point are given in Table 1.
Figure 4. Variability P in Modern Distribution and Ecological-phytocenotic Features of Platanthera chlorantha (Cust.) Rchb. in the Republic of Adygea
Figure 4. Variability P. chlorahtha: 1 – height; 2 – length of inflorescence; 3 – the length of the bottom sheet; 4 – the width of the bottom sheet; 5 – number of stem leaves; 6 – the length of the lower bract; 7 – the width of the lower bract; 8 – number of flowers; 9 – spur length of bottom flower; 10 – length of the ovary; 11 – the length of the lip; 12 – the width of the lip.
Figure 1 in Ecological factors determining the distribution patterns of Cyrtanthus nutans R.A.Dyer (Amaryllidaceae) in northwestern KwaZulu-Natal, South Africa
Figure 1. Range and distribution of C. nutans in five main areas within northwestern KwaZuluNatal (Area 1 = Dundee central; Area 2 = eastern Dundee; Area 3 = northeastern Dundee; Area 4 = Rorkes Drift and Area 5 = Wasbank).
Fig. 4 in A social beauty: distribution, ecology and conservation of Iris oratoria in the Central Mediterranean Region (Insecta: Mantodea)
Fig. 4 – Comparison of the Extent Of Occurrence (EOO) of the Mediterranen populations of Iris oratoria calculated on scientific records (red polygon) and on records from citizen science (green polygon). Base map: OpenStreetMap.
Fig. 2 in A social beauty: distribution, ecology and conservation of Iris oratoria in the Central Mediterranean Region (Insecta: Mantodea)
Fig. 2 – The strip transect at the Dune di Giovino (southern Italy, Calabria) in a retrodunal area (left) and a sub-adult male of Iris oratoria on Artemisia vulgaris.
Fig. 1 in A social beauty: distribution, ecology and conservation of Iris oratoria in the Central Mediterranean Region (Insecta: Mantodea)
Fig. 1 – Presence records of Iris oratoria in the Central Mediterranean region, from the original records here presented (orange dots), collecting records from preserved specimens in museum collections and literature (red dots), from occasional records not related to confirmed populations (blue dots) and from citizen-science observations (green dots). Base map: OpenStreetMap.
Figure 28 in New data on distribution and ecology of seven species of Euscorpius Thorell, 1876 (Scorpiones: Euscorpiidae)
Figure 28: E. flavicaudis collecting sites. Liguria, Tuscany (Italy) and Var (France): 1. Fayence; 2. Mont Faron; 3. Andora Castello; 4. Toirano; 5. Finale Ligure; 6. Levigliani; 7. Castelfalfi.
Figure 24 in New data on distribution and ecology of seven species of Euscorpius Thorell, 1876 (Scorpiones: Euscorpiidae)
Figure 24: E. naupliensis collecting sites. Zakynthos Island (Greece): 1. Skoulikado; 2. Louha; 3. Anafonitria; 4. Volimes; 5. near Volimes.
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.