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8,119 results for “species distribution”
Data from: Odonate species occupancy frequency distribution and abundance – occupancy relationship patterns in temporal and permanent water bodies in a subtropical area
<p>This paper investigates species richness and species occupancy frequency distributions (SOFD) as well as patterns of abundance-occupancy relationship (SAOR) in Odonata (dragonflies and damselflies) in a subtropical area. A total of 82 species and 1983 individuals were noted from 73 permanent and temporal water bodies (lakes and ponds) in the Pampa biome in southern Brazil. Odonate species occupancy ranged from 1 to 54. There were few widely distributed generalist species and several specialist species with a restricted distribution. About 70% of the species occurred in less than 10% of the water bodies, yielding a surprisingly high number of rare species, often making up the majority of the communities. No difference in species richness was found between temporal and permanent water bodies. Both temporal and permanent water bodies had odonate assemblages that fitted best with the unimodal satellite SOFD pattern. It seems that unimodal satellite SOFD pattern frequently occurred in the aquatic habitats. The SAOR pattern was positive and did not differ between permanent and temporal water bodies. Our results are consistent with a niche-based model rather than a metapopulation dynamics model.</p>
Image 5 in Distribution of six little known plant species from Arunachal Pradesh, India
Image 5. Plectocomia himalayana Griff.
Image 1 in Distribution of six little known plant species from Arunachal Pradesh, India
Image 1. Begonia silhetensis (A. DC.) C.B. Clarke
Image 3. Larsenianthus arunachalensis M in Distribution of six little known plant species from Arunachal Pradesh, India
Image 3. Larsenianthus arunachalensis M.Sabu, Sanoj & T. Rajesh Kumar
Image 4. Larsenianthus assamensis S in Distribution of six little known plant species from Arunachal Pradesh, India
Image 4. Larsenianthus assamensis S.Dey, Mood & S. Choudhury
Image 2 in Distribution of six little known plant species from Arunachal Pradesh, India
Image 2. Dalbergia thomsonii Benth.
Image 6 in Distribution of six little known plant species from Arunachal Pradesh, India
Image 6. Tricarpelema glanduliferum (J. Joseph & R.S. Rao) R.S. Rao
Potential distributional shifts in North America of allelopathic invasive plant species under climate change models
<p>Occurrence data for invaive species used in ecological niche modeling for predictive studies. These data are cleaned to removed data with duplicates, incomplete coordinates, unlikely coordinates (e.g., 0,0), or those lacking environmental data were removed using the scrubr v.0.1.1 package in R (Chamberlain, 2016). Points falling outside of the respective training region for each species were also removed. These data represent downloads from iDigBio and GBIF.</p>
Energy-water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperms species richness in China
<p>Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large-scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human-induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human-induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km<sup>2 </sup>spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (i) all species, (ii) endemic species, and (iii) non-endemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (i) energy-water, (ii) climatic seasonality, (iii) habitat heterogeneity, and (iv) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy-water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat-heterogeneity has a better influence on non-endemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.</p>
Figure 1 in A contribution on the Middle American milliped family Rhachodesmidae (Polydesmida: Leptodesmidea: Rhachodesmoidea): description of Tiphallus torreon n. sp., the first species from Coahuila, Mexico; first records from Belize; and depiction of the (super)familial distribution
Figure 1. Tiphallus torreon, habitus photo showing in vivo dorsal coloration.
Figure 4 in The Herpetofauna from Ilha Grande (Angra dos Reis, Rio de Janeiro, Brazil): updating species composition, richness, distribution and endemisms
Figure 4. Specimen of Corallus hortulanus (not collected) found in Vila do Aventureiro village, Ilha Grande (Photo by F.B.S. Telles).
Figure 3 in The Herpetofauna from Ilha Grande (Angra dos Reis, Rio de Janeiro, Brazil): updating species composition, richness, distribution and endemisms
Figure 3. New records of snakes for Ilha Grande. (A) Dipsas indica (Photo by D. Cunha-Passos); (B) Echinantera cephalostriata (Photo by P. Fatorelli).
Figure 1 in The Herpetofauna from Ilha Grande (Angra dos Reis, Rio de Janeiro, Brazil): updating species composition, richness, distribution and endemisms
Figure 1. Image of Ilha Grande with the localities where the species were recorded, and their respective density points of species. CAX = Caxadaço trail; CDM = Costão do Demo, RBEPS; EAD = Abraão-Dois Rios road; FPS = Forest of Praia do Sul; JAR = Jararaca trail, Dois Rios; LPM = Lopes Mendes; PAP = Pico do Papagaio; PRN = Parnaioca trail; RPS = Restinga of Praia do Sul; VAB = Vila do Abraão; VAV = Vila do Aventureiro; VDR = Vila Dois Rios.
Figure 2 in The Herpetofauna from Ilha Grande (Angra dos Reis, Rio de Janeiro, Brazil): updating species composition, richness, distribution and endemisms
Figure 2. New records of amphibians for Ilha Grande. (A) Ischnocnema bolbodactyla; (B) Leptodactylus latrans; (C) Leptodactylus flavopictus (Photos by F.B.S. Telles).
Figure 8 in Neotropical felid specimens at the Museu Paraense Emilio Goeldi: species, distribution, and morphometric data
Figure 8. Representative of three comparable species of the genus Leopardus represented in the MPEG's mammal collection, showing patterns of ground coat and markings: Left: Leopardus tigrinus (MPEG 42973, BR 429, Costa Marques, Rondônia, Brazil); middle: L. guttulus (MPEG 22183, BR 290, km 141 [= 'Rio Pardo'], Eldorado do Sul, Rio Grande do Sul state, Brazil); right: L. wiedii (MPEG, 428, São João do Araguaia [São João], Rio Araguaia, Pará state, Brazil). Scale: 10 cm.
Figure 1 in Neotropical felid specimens at the Museu Paraense Emilio Goeldi: species, distribution, and morphometric data
Figure 1. Map of biomes of the Brazil with all wild felid specimens, with accurate locality data, housed in the Museu Paraense Emilio Goeldi.
Fig. 4 in Wild bees (Anthophila) of Porto Santo (Madeira Archipelago) and their habitats: species diversity, distribution patterns and bee-plant network *
Fig. 4: Bipartite graph of the bee-plant network of Porto Santo.
Fig. 24 in Contribution to the knowledge of the caddisfly fauna (Trichoptera) of Iran: description of new species and new distributional data
Fig. 24. Map of Iran with indicated localities no. 1-28 (see text for details).
Fig. 22 in Taxonomy, distribution and biology of selected European Dinax, Strongylogaster and Taxonus species (Hymenoptera: Symphyta)
Fig. 22. Known localities of Strongylogaster baikalensis Naito, 1990 in the Czech Republic.
Fig. 21 in Taxonomy, distribution and biology of selected European Dinax, Strongylogaster and Taxonus species (Hymenoptera: Symphyta)
Fig. 21. Known localities of Dinax ermak (Zhelochovtsev, 1968) in the Czech Republic.
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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)
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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.