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326 results for “biogeographical regionalization”
Data from: On the importance of habitat continuity for delimiting biogeographic regions and shaping richness gradients
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Regional climates shape the biogeographic history of a broadly distributed freshwater crab species complex
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Data from: The world’s biogeographical regions revisited: global patterns of endemism in Tipulidae (Diptera)
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Evaluating the boundaries of marine biogeographic regions of the Southwestern Atlantic using halacarid mites (Halacaridae), meiobenthic organisms with a low dispersal potential
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Regional and local environment drive biogeographic patterns in intertidal microorganisms
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Diversification dynamics in the Neotropics through time, clades and biogeographic regions
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Evoregions: mapping shifts in phylogenetic turnover across biogeographic regions
<p>1. Biogeographic regionalization offers context to the geographical evolution of clades. The positions of bioregions inform both the spatial location of clusters in species distribution and where their most important boundaries are. Nevertheless, defining bioregions based on species distribution alone only incidentally recover regions that are important during the evolution of the focal group. The extent to which bioregions correspond to centers of independent diversification depends on how clusters of species composition naturally reflect the radiation of single clades, which is not the case when mixed colonization occurred.</p> <p>2. Here, we showed that using phylogenetic turnover based on fuzzy sets, instead of species composition, led to adequate detection of evolutionarily important bioregions, that is, regions that account for the independent diversification of lineages. Mapping those evoregions in the phylogenetic tree quickly reveals the timing and location of major shifts of biogeographic regions. Moreover, evolutionary transition zones are easily mapped, and permits the recognition of regions with high phylogenetic overlap.</p> <p>3. Our results using the global radiation of rats and mice (Muroidea) recovered four evoregions—three major evolutionary arenas corresponding to the Neotropics, a Nearctic-Siberian, and a Paleotropical-Australian evoregion, and a fourth and fuzzy Afro-Palearctic evoregion. Transition zones among evoregions were minimized when compared to other methods considering or not phylogenetic information, that is, the affiliation of cells to their assigned region was higher using evoregions than other approaches. Such higher affiliation values result from the lower phylogenetic overlap within evoregions, as expected when single radiations are accounted for as best as possible.</p> <p>4. Evoregions is a useful framework whenever the question is related to the identification of the most important centers of a group's diversification history and its evolutionary transitions zones.</p>
phyloregion: R package for biogeographic regionalization and spatial conservation
<ol> <li>Biogeographical regionalization is the classification of regions in terms of their biotas and is key to understanding biodiversity patterns across the world. Previously, it was only possible to perform analysis of biogeographic regionalization on small datasets, often using tools that are difficult to replicate.</li> <li>Here, we present phyloregion, a package for the analysis of biogeographic regionalization and spatial conservation in the R computing environment, tailored for mega phylogenies and macroecological datasets of ever-increasing size and complexity.</li> <li>Compared to available packages, phyloregion is three to four orders of magnitude faster and memory efficient for cluster analysis, determining optimal number of clusters, evolutionary distinctiveness of regions, as well as analysis of more standard conservation measures of phylogenetic diversity, phylogenetic endemism, and evolutionary distinctiveness and global endangerment.</li> <li>A case study for zoogeographic regionalization of 9574 species of squamate reptiles (amphisbaenians, lizards, and snakes) across the globe, reveals their evolutionary affinities using visualization tools that allow rapid identification of patterns and underlying processes with user-friendly colours–for example–indicating the levels of differentiation of the taxa in different regions.</li> <li>Ultimately, phyloregion would facilitate rapid biogeographic analyses that accommodates the ongoing mass-production of species occurrence records and phylogenetic datasets at any scale and for any taxonomic group into completely reproducible R workflows.</li> </ol>
FIGURE 1 0 in The unknown diversity of the genus Characidium (Characiformes: Crenuchidae) in the Chocó biogeographic region, Colombian Andes: Two new species supported by morphological and molecular data
FIGURE 1 0 Typical habitat of C. dule, Arquia river
FIGURE 6 C. dule n in The unknown diversity of the genus Characidium (Characiformes: Crenuchidae) in the Chocó biogeographic region, Colombian Andes: Two new species supported by morphological and molecular data
FIGURE 6 C. dule n. sp. IMCN 8941, Holotype 37.4 mm LS. Scale bar = 1 cm
FIGURE 9 C. dule n in The unknown diversity of the genus Characidium (Characiformes: Crenuchidae) in the Chocó biogeographic region, Colombian Andes: Two new species supported by morphological and molecular data
FIGURE 9 C. dule n. sp. IMCN 8929, Paratype. Live specimen. Scale bar = 1 cm
FIGURE 5 in The unknown diversity of the genus Characidium (Characiformes: Crenuchidae) in the Chocó biogeographic region, Colombian Andes: Two new species supported by morphological and molecular data
FIGURE 5 Typical habitat of C. dule, Ingará River, San José Del Palmar, Chocó, Colombia
Supplementary material 1 from: Doorenweerd C, Ekayanti A, Rubinoff D (2020) The Dacini fruit fly fauna of Sulawesi fits Lydekker's line but also supports Wallacea as a biogeographic region (Diptera, Tephritidae). ZooKeys 973: 103-122. https://doi.org/10.3897/zookeys.973.55327
Table S1
Figures 26-31 from: Doorenweerd C, Ekayanti A, Rubinoff D (2020) The Dacini fruit fly fauna of Sulawesi fits Lydekker's line but also supports Wallacea as a biogeographic region (Diptera, Tephritidae). ZooKeys 973: 103-122. https://doi.org/10.3897/zookeys.973.55327
Figures 26-31 Dacus pullus26 dorsal view 27 frontal view of the face 28 lateral view 29 posterior view of the abdomen showing the ceromae 30 dissected wing 31 lateral close-up of the genitalia.
Figures 22-25 from: Doorenweerd C, Ekayanti A, Rubinoff D (2020) The Dacini fruit fly fauna of Sulawesi fits Lydekker's line but also supports Wallacea as a biogeographic region (Diptera, Tephritidae). ZooKeys 973: 103-122. https://doi.org/10.3897/zookeys.973.55327
Figures 22-25 The two specimens of Bactrocera carambolae that represent the first records for Sulawesi, photographed in ethanol (wings were removed) 22 dorsal view of specimen ms08439 23 lateral view of specimens ms08439 24 dorsal view of specimen ms10710 25 lateral view of specimen ms10710. Both specimens have the typical rectangular black mark on the lateral sides of the fourth abdominal segment, but lack the black mark on the fore femur, which can further help to distinguish B. carambolae from B. dorsalis.
Figures 14- 15 from: Doorenweerd C, Ekayanti A, Rubinoff D (2020) The Dacini fruit fly fauna of Sulawesi fits Lydekker's line but also supports Wallacea as a biogeographic region (Diptera, Tephritidae). ZooKeys 973: 103-122. https://doi.org/10.3897/zookeys.973.55327
Figures 14- 15 Maximum Likelihood trees based on COI (14) and EF1-alpha (15) DNA sequence data for Dacus longicornis, with D. pullescens Munro and D. vertebratus Bezzi as outgroups. Branch support values are rapid bootstrap values and approximate-likelihood ratio test values, scale bar indicates substitutions per site. Full details on the samples can be found in BOLD dataset DOI: http://dx.doi.org/10.5883/DS-DACSU.
Figures 20- 21 from: Doorenweerd C, Ekayanti A, Rubinoff D (2020) The Dacini fruit fly fauna of Sulawesi fits Lydekker's line but also supports Wallacea as a biogeographic region (Diptera, Tephritidae). ZooKeys 973: 103-122. https://doi.org/10.3897/zookeys.973.55327
Figures 20- 21 Maximum Likelihood trees based on COI (20) and EF1-alpha (21) DNA sequence data for Bactrocera melastomatos and allied species, using B. lombokensis Drew & Hancock and B. digressa Radhakrishnan as outgroup. Branch support values are rapid bootstrap values and approximate-likelihood ratio test values, scale bar indicates substitutions per site. Full details on the samples can be found in BOLD dataset DOI: http://dx.doi.org/10.5883/DS-DACSU.
Figure 1 from: Doorenweerd C, Ekayanti A, Rubinoff D (2020) The Dacini fruit fly fauna of Sulawesi fits Lydekker's line but also supports Wallacea as a biogeographic region (Diptera, Tephritidae). ZooKeys 973: 103-122. https://doi.org/10.3897/zookeys.973.55327
Figure 1 Map of Sulawesi and neighboring areas showing the four sampling localities with orange spots; the three localities in South Sulawesi were in close proximity to each other. Two typical biogeographical boundaries are indicated with dotted lines: Wallace's line and Lydekker's line. Land masses west of Wallace's line were connected during ice ages as Sunda, east of Lydekker's line land masses were connected as Sahul. Islands in between the two biogeographical boundaries were never connected by land and are jointly known as Wallacea.
Figures 16-19 from: Doorenweerd C, Ekayanti A, Rubinoff D (2020) The Dacini fruit fly fauna of Sulawesi fits Lydekker's line but also supports Wallacea as a biogeographic region (Diptera, Tephritidae). ZooKeys 973: 103-122. https://doi.org/10.3897/zookeys.973.55327
Figures 16-19 Sulawesi Bactrocera melastomatos resemble sympatric Bactrocera usitata16 specimen ms09144 B. usitata, dorsal view 17 close up of abdomen of ms09144 18 specimen ms08838 B. melastomatos, dorsal view 19 close up of abdomen of ms08838.
Figures 8-13 from: Doorenweerd C, Ekayanti A, Rubinoff D (2020) The Dacini fruit fly fauna of Sulawesi fits Lydekker's line but also supports Wallacea as a biogeographic region (Diptera, Tephritidae). ZooKeys 973: 103-122. https://doi.org/10.3897/zookeys.973.55327
Figures 8-13 Two forms of Dacus longicornis8D. longicornis collected in Bangladesh, Pabna district, 30-ix-3-x-2013 Leg. M. A. Hossain 9D. longicornis collected in Bangladesh, Maulvi Bazar Rainforest resort, Leg. L. Leblanc & M. A. Hossain 10 specimen ms08424, collected in Sulawesi, with a faint medial postsutural yellow vitta 11 specimen ms08432, collected in Sulawesi 12 specimen ms08428, collected in Sulawesi 13 specimen ms08421, collected in Sulawesi.
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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.