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638 results for “biomes”
The biome inventory – standardizing global biogeographical land units
<p class="MsoNormal">The subdivision of the Earth's terrestrial surface into different biomes, ecozones, bio-climatic realms or other large ecological land units is an essential reference for global biogeographical and ecological studies. Various classification schemes exist. They differ significantly in terms of the considered criteria for classification and the underlying methodology of class assignments. Evident divergences between global biome concepts are elusive, weakening hereon based analyses and assumptions. Compilation and standardization are essential for obtaining a framework that enables the comparison of different products. To address this need, we created a catalogue of standardized categorial maps comprising 31 different global products based on various methodological approaches. Those were processed individually to facilitate their use in large-scale biogeographical and ecological analyses.</p>
Projected climatic changes lead to biome changes in areas of previously constant biome
<p class="Indentedmaintext"><b>Aim: </b>Recent studies in southern Africa identified past biome stability as an important predictor of biodiversity. We aimed to assess the extent to which past biome stability predicts present global biodiversity patterns, and the extent to which projected climatic changes may lead to eventual biome changes in areas with constant past biome.</p> <p class="Indentedmaintext"><b>Location: </b>Global.</p> <p class="Indentedmaintext"><b>Taxon: </b>Spermatophyta; terrestrial vertebrates.</p> <p class="Indentedmaintext"><b>Methods: </b>Biome constancy was assessed and mapped using results from 89 dynamic global vegetation model simulations, driven by outputs of palaeoclimate experiments spanning the past 140 ka. We tested the hypothesis that terrestrial vertebrate diversity is predicted by biome constancy. We also simulated potential future vegetation, and hence potential future biome patterns, and quantified and mapped the extent of projected eventual future biome change in areas of past constant biome.</p> <p class="Indentedmaintext"><b>Results: </b>Approximately 11% of global ice-free land had a constant biome since 140 ka. Aside from areas of constant Desert, many areas with constant biome support high species diversity. All terrestrial vertebrate groups show a strong positive relationship between biome constancy and vertebrate diversity in areas of greater diversity, but no relationship in less diverse areas. Climatic change projected by 2100 commits 46–66% of global ice-free land, and 34–52% of areas of past constant biome (excluding areas of constant Desert) to eventual biome change.</p> <p class="Indentedmaintext"><b>Main conclusions: </b>Past biome stability strongly predicts vertebrate diversity in areas of higher diversity. Future climatic changes will lead to biome changes in many areas of past constant biome, with profound implications for biodiversity conservation. Some projected biome changes will result in substantial reductions in biospheric carbon sequestration and other ecosystem services.</p>
Dominant Rural Technological Trajectories (TTs) dataset at municipality level of the Amazon Biome
<p>This dataset contains the dominant technological trajectories (TTs) of the Amazon Biome municipalities for the years of 1995, 2006 and 2017. The dominant trajectory is the one, among the six identified by Costa (2021), that is economically most important in the municipality. The relative share of the Gross Value of Rural Production of the trajectory in the total Gross Value of Rural Production in the municipality was taken as a proxy of economic importance. From the tabulation of the datasets in Costa (2021), the dominant technological trajectory was identified, it means, considering the methodology used, which of the six TTs was responsible for over 50% of the municipal Gross Value of Rural Production. The dominant TTs were calculated using the official municipal grid for the year the agricultural census was carried out.</p> <p>The dataset is organized as a .csv table, for each year (1995, 2006 and 2017), with a geographic key for each municipality (6-digit municipality code, 2-digit state code), that can be easily linked with municipality available shapefiles and other datasets. </p> <p>References:</p> <p>Costa, F. A. Structural diversity and change in rural Amazonia: a comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). <strong>Nova econ. </strong>31 (02), May-Aug 2021, doi:10.1590/0103-6351/6373.</p>
Figure 5 in A three-year Herpetofauna survey from one of the largest remnants of the Atlantic Rainforest Biome (Reserva Natural Vale)
Figure 5. Some of the reptile species we recorded in the Vale Natural Reserve, municipality of Linhares, Espírito Santo, southeastern Brazil: (a) Acanthochelys radiolata, (b) Chelonoidis carbonaria, (c) Chelonoidis denticulatus, (d) Rhinoclemmys punctularia, (e) Brasiliscincus agilis, (f) Psychosaura macrorhyncha, (g) Norops ortonii, (h) Polychrus marmoratus, (i) Strobilurus torquatus, (j) Tropidurus torquatus, (k) Ameivula nativo, (l) Salvator merianae.
Figure 4 in A three-year Herpetofauna survey from one of the largest remnants of the Atlantic Rainforest Biome (Reserva Natural Vale)
Figure 4. Accumulation (black line) and rarefaction curves (gray line) of the amphibian species recorded during the 23 sampling campaigns from 2015 to 2018 in the Vale Natural Reserve, municipality of Linhares, Espírito Santo, southeastern Brazil.
Figure 1 in A three-year Herpetofauna survey from one of the largest remnants of the Atlantic Rainforest Biome (Reserva Natural Vale)
Figure 1. Location of the Vale Natural Reserve, municipality of Linhares, Espírito Santo, southeastern Brazil, showing the location of the plots and the vegetation types present in the reserve: (a) Coastal plain forest, (b) Permanent swamp, (c) Sandy soil forest, (d) Natural grassland.
Figure 7 in A three-year Herpetofauna survey from one of the largest remnants of the Atlantic Rainforest Biome (Reserva Natural Vale)
Figure 7. Accumulation (black line) and rarefaction curves (gray line) of the reptile species recorded during the 23 sampling campaigns from 2015 to 2018 in the Vale Natural Reserve, municipality of Linhares, Espírito Santo, southeastern Brazil.
Figure 3 in A three-year Herpetofauna survey from one of the largest remnants of the Atlantic Rainforest Biome (Reserva Natural Vale)
Figure 3. Some of the anuran species we recorded in the Vale Natural Reserve, municipality of Linhares, Espírito Santo, southeastern Brazil: (a) Trachycephalus mesophaeus, (b) Leptodactylus fuscus, (c) Leptodactylus latrans, (d) Leptodactylus mystacinus, (e) Physalaemus aguirrei, (f) Physalaemus gr. signifer, (g) Chiasmocleis capixaba, (h) Chiasmocleis schubarti, (i) Dasypops schirchi, (j) Stereocyclops incrassatus, (k) Proceratophrys laticeps, (l) Phyllomedusa burmeisteri.
Figure 6 in A three-year Herpetofauna survey from one of the largest remnants of the Atlantic Rainforest Biome (Reserva Natural Vale)
Figure 6. Some of the reptile species we recorded in theVale Natural Reserve,municipality of Linhares, Espírito Santo, southeastern Brazil:(a) Amerotyphlops brongersmianus, (b) Corallus hortulanus, (c) Chironius foveatus, (d) Chironius fuscus, (e) Leptophis ahaetulla, (f) Oxybelis aeneus, (g) Spilotes sulphureus, (h) Erythrolamprus miliaris, (i) Siphlophis compressus, (j) Thamnodynastes hypoconia, (k) Micrurus corallinus preying on A. brongersmianus, (l) Bothrops jararaca.
Figure 2 in A three-year Herpetofauna survey from one of the largest remnants of the Atlantic Rainforest Biome (Reserva Natural Vale)
Figure 2. Some of the anuran species we recorded in the Vale Natural Reserve, municipality of Linhares, Espírito Santo, southeastern Brazil: (a) Rhinella crucifer, (b) Rhinella diptycha, (c) Haddadus binotatus, (d) Aparasphenodon brunoi, (e) Boana semilineata, (f) Dendropsophus elegans, (g) Dendropsophus seniculus, (h) Ololygon agilis, (i) Phyllodytes luteolus, (j) Scinax eurydice, (k) Scinax sp., (l) Sphaenorhynchus planicola.
Figure S4 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome
Figure S4. Rarefaction curve for both studied fragments in the Atlantic Forest biome, Brazil. Sample coverage is the proportion of the total number of individuals that belong to the species detected in the sample. F1 = Fragment 1 (28°08′38″S, 54°45′36″W); F2 = Fragment 2 (28°07′33″S, 54°44′57″W).
Figure 3 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome
Figure 3. Variables coefficients and their confidence intervals in the models selected (with ΔAIC ≤ 2) for each small mammal species. (A) Akodon montensis; (B) Oligoryzomys nigripes; (C) Sooretamys angouya; (D) Didelphis albiventris. PC1GC = first axis of the PCA for soil variables; PC2GC = second axis of the PCA for soil variables; PC1VS = first axis of the PCA for vegetation structure; PC2VS = second axis of the PCA for vegetation structure.
FIG. 6 in New genus and species of Seirini (Collembola, Entomobryidae) from Caatinga Biome, Northeastern Brazil
FIG. 6. — Dorsal macrochaetae distribution of Tyrannoseira sex n. sp.
FIG. 5. — Tyrannoseira sex n in New genus and species of Seirini (Collembola, Entomobryidae) from Caatinga Biome, Northeastern Brazil
FIG. 5. — Tyrannoseira sex n. sp., dorsal chaetotaxy of: A, second and B, third abdominal segments.
Fig. 4 in Population analysis of white grubs (Coleoptera: Melolonthidae) throughout the Brazilian Pampa biome
Fig. 4. Melolonthidae species and number of locations that each species was found.
Fig. 2 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 2. Spatial distribution of the species found in the Cruzeiro do Sul rural district.
Fig. 1 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 1. Localization of the study areas in the Atlantic Forest biome, São Paulo state – Brazil.
Fig. 6 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 6. Adjusted Semi-variograms of the Simpson diversity index.
Fig. 3 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 3. Spatial distribution of the species found in the Vale das Cigarras rural districts.
Fig. 1 in Distribution, habitat use and plant associations of Moluchia brevipennis (Saussure, 1864) (Blattodea: Ectobiidae): an endemic cockroach from Chilean Mediterranean Matorral biome
Fig. 1. Registration points and theoretical presence extension of M. brevipennis.
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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.