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Figure 4 in Variation in dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae) assemblages in a tropical forest remnant from a Mexican National Park
Figure 4. Rank-abundance curves of the dung beetle assemblages collected with four different baits, pointing out the dominant species in each. U.mic = Uroxys microcularis; C.lae = Copris laeviceps; A.rod = Ateuchus rodriguezi; C.vaz. = Canthon vazquezae; P.end = Phanaeus endymion.
The role of island physiography and oceanographic factors in shaping species richness and turnover of nesting seabird assemblages on islands across the southeastern Pacific
<p>For seabirds, food supplies and nest sites are largely driven by oceanographic gradients and island habitats, respectively. Research into seabirds' ecological roles in insular ecosystems is crucial to understanding processes that structure seabird nesting assemblages. We examined the influence of island physiography and oceanographic factors on the spatial variation in α and β-diversity of nesting seabird assemblages.<br> <br> <strong>Location</strong><br> Southeastern Pacific Ocean.<br> <br> <strong>Taxon</strong><br> Birds<br> <br> <strong>Methods</strong><br> We compiled data from 53 seabirds breeding on 41 coastal and oceanic islands using different sources: our field records, online databases, environmental reports, and literature. We used generalized linear models (GLM) to describe the effect of island physiography (area, elevation, and isolation) and oceanographic factors (surface temperature, salinity, and primary productivity) on seabird species richness (α-diversity). We applied multivariate GLM to test the effects of physiographic and oceanographic predictors on species composition (β-diversity). We used Jaccard dissimilarities on species occurrences per island to calculate β-diversity partitioned into turnover and nestedness. Polynomial models allowed us to model these metrics against geographical and environmental gradients and so analyze patterns in seabird β-diversity across spatial scales.<br> <br> <strong>Results </strong><br> Species richness was highest in Galápagos, Pitcairn, and Rapa Nui. Changes in seabird α-diversity across islands were determined by island area and distance to South America but not by oceanographic variables. Physiographic and oceanographic factors were significant in determining β-diversity. Changes in β-diversity were mostly due to species replacement (β-turnover) across three major island Systems (Galápagos Archipelago, Chilean coastal islands, and oceanic islands of the southeastern Pacific). The contribution of β-nestedness was restricted to small scales (within archipelagos).<br> <br> <strong>Main conclusions</strong><br> Physiographic and oceanographic factors explain species diversity of seabird assemblages on islands of the southeastern Pacific. Oceanographic variables did not affect species richness but significantly influenced species composition. Change in species composition reflects gradients across three marine biogeographical realms: Temperate South, Eastern Indo-Pacific, and Tropical Eastern Pacific. The low degree of species nestedness may reflect multiple evolutionary origins.</p>
Figure 3 in Structural differences in mammal assemblages between savanna ecosystems of the Colombian Llanos
Figure 3. Species accumulation curves at the three savanna ecosystems of Colombian Llanos. Dotted lines show 95% confidence interval.
Figure 2 in Structural differences in mammal assemblages between savanna ecosystems of the Colombian Llanos
Figure 2. Some species recorded in Savanna ecosystems in the Colombian Llanos: (A) Hydrochoerus hydrochaeris; (B) Myrmecophaga tridactyla; (C) Puma yagouaroundi; (D) Cuniculus paca; (E) Puma concolor; (F) Odocoileus cariacou; (G) Leopardus pardalis; (H) Tamandua tetradactyla; (I) Pecari tajacu.
Figure 1 in Structural differences in mammal assemblages between savanna ecosystems of the Colombian Llanos
Figure 1. Geographical location of the localities studied in the three savanna ecosystems sampled in the Colombian Llanos: (A) SE-1., floodplain savannas (Arauca); (B) SE-2., aeolian savannas (Casanare); (C) SE-3., high-plain savannas (Vichada).
Figure 6 in Structural differences in mammal assemblages between savanna ecosystems of the Colombian Llanos
Figure 6. Frequency of tropic guilds recorded at the three savanna ecosystems of the Colombian Llanos.
Figure 5 in Structural differences in mammal assemblages between savanna ecosystems of the Colombian Llanos
Figure 5. Comparisons of mean species richness and total number of events of medium- and large-sized mammals among the three savanna ecosystems of the Colombian Llanos. Boxplots with different letters denotes significant differences among means (p <0.05).
Figure 4 in Structural differences in mammal assemblages between savanna ecosystems of the Colombian Llanos
Figure 4. Rank-abundance curves of medium- and large-sized mammals recorded at the three savanna ecosystems of the Colombian Llanos. Species acronyms are listed in Table 1.
Figure 4 in Use of microhabitats affects butterfly assemblages in a rural landscape
Figure 4. Results of integrated coverage-based rarefaction-extrapolation (Chao & Jost, 2012) of butterfly richness between 18 sampled points in a rural landscape in Southern Brazil (confidence interval = 95%). Continuous lines (rarefaction), dotted lines (extrapolation).
Figure 5 in Use of microhabitats affects butterfly assemblages in a rural landscape
Figure 5. Non-metric multidimensional scaling (NMDS), plots representing butterfly assemblages composition measured in distinct types of microhabitats (abandoned edge, road edge, farmland edge and forest interior) in a rural landscape in Southern Brazil. a) Including forest interior sampled sites (Stress = 0.11); b) Excluding forest interior sampled sites (Stress = 0.14). Forest interior butterfly communities are distinct from all edge communities, but all edge habitats support similar butterfly communities despite microhabitat differences (even when interior samples are removed from the data analysis).
Figure 3 in Use of microhabitats affects butterfly assemblages in a rural landscape
Figure 3. Results of integrated coverage-based rarefaction-extrapolation (Chao & Jost, 2012) of butterfly richness between microhabitat types in a rural landscape in Southern Brazil (confidence interval = 95%). Continuous lines (rarefaction), dotted lines (extrapolation).
Figure 2 in Use of microhabitats affects butterfly assemblages in a rural landscape
Figure 2. Characterization of the sampled microhabitats in a rural landscape in Southern Brazil. (a, b) forest interior; (c, d) abandoned edge; (e, f) farmland edge; (g, h) road edge.
Figure 1 in Use of microhabitats affects butterfly assemblages in a rural landscape
Figure 1. Location of the study area in the Joaçaba Municipality, Santa Catarina State, Brazil. (a) Fragments sampled; (b-c) Distribution of collection points of A, B and C fragments.
Fig. 3 in Influence Of Environmental Cycles Upon A Seagrass Caridean Shrimp Assemblage
Fig. 3. Mean (+S.E.) shrimp abundance and diversity (Hill's N2) during the lunar cycle.
Fig. 3 in Diversity And Assemblage Patterns Of Juvenile And Small Sized Fishes In The Nearshore Habitats Of The Gulf Of Thailand
Fig. 3. Species richness of fish samples in each habitat during the study period.
Fig. 20 in Taxonomic Composition And Systematics Of Late Cretaceous Lizard Assemblages From Ukhaa Tolgod And Adjacent Localities, Mongolian Gobi Desert
Fig. 20. Pyramicephalosaurus cherminicus: A, IGM 3/131, incomplete right maxilla with
Fig. 17 in Taxonomic Composition And Systematics Of Late Cretaceous Lizard Assemblages From Ukhaa Tolgod And Adjacent Localities, Mongolian Gobi Desert
Fig. 17. Gobinatus arenosus: A–D, IGM 3/126, incomplete skull with mandibles from Khulsan,
Fig. 9 in Taxonomic Composition And Systematics Of Late Cretaceous Lizard Assemblages From Ukhaa Tolgod And Adjacent Localities, Mongolian Gobi Desert
Fig. 9. Mimeosaurus crassus: A–C, IGM 3/
Fig. 34 in Taxonomic Composition And Systematics Of Late Cretaceous Lizard Assemblages From Ukhaa Tolgod And Adjacent Localities, Mongolian Gobi Desert
Fig. 34. Anguimorpha, Varanoidea, Aiolosaurus oriens, new genus and species: A–C, IGM 3/171,
Fig. 1 in Taxonomic Composition And Systematics Of Late Cretaceous Lizard Assemblages From Ukhaa Tolgod And Adjacent Localities, Mongolian Gobi Desert
Fig. 1. Map of Mongolian Gobi Desert showing major fossil localities.
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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.