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3,421 results for “Amazon”
Data package from "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images"
<p>This data package contains the very high resolution maps of canopy palms from the paper "Regional Mapping and Spatial Distribution Analysis of Canopy Palms in an Amazon Forest Using Deep Learning and VHR Images". These maps have been produced with two GeoEye-1 very high resolution images (0.5 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. The total size of the decompressed archive is 2.56 Go and is distributed in two shapefiles, one for each GeoEye-1 image. When using this dataset, please cite the original article https://doi.org/10.3390/rs12142225</p>
Lightning-caused disturbance in the Peruvian Amazon
<p>Lightning is a major agent of disturbance in tropical terrestrial ecosystems, but its effects often are overlooked or misidentified in lowland forests. We used an <a>unmanned</a> aerial vehicle (i.e., drone) to locate 12 probable lightning strike sites in ca. 47 ha of forest in the Peruvian Amazon. Subsequent ground-based surveys of the 10 accessible sites revealed 7 that were unambiguously caused by lightning. The seven sites included 121 lightning-damaged trees, 45 of which were dead. Large trees (>60 cm in diameter) were disproportionately affected by lightning. The numbers of trees damaged and their size distribution were comparable to results from a comprehensive study of lightning damage in Panama. By contrast, post hoc surveys of lightning gaps in Brazil and Malaysia documented 3-5 times more dead trees per strike, suggesting that gap-focused surveys are biased towards larger disturbances. These findings contribute to a growing body of evidence that lightning is an important disturbance pantropically, and that accurately documenting the effects of lightning requires reliable identification of lightning strike locations.</p>
Data from: Avian ecological succession in the Amazon: a long-term case study following experimental deforestation
<p>Approximately 20% of the Brazilian Amazon has now been deforested, and the Amazon is currently experiencing the highest rates of deforestation in a decade, leading to large-scale land-use changes. Roads have consistently been implicated as drivers of ongoing Amazon deforestation and may act as corridors to facilitate species invasions. <span>Long-term data, however, are necessary to determine how ecological succession alters avian communities following deforestation and whether established roads lead to a constant influx of new species. </span> </p> <p>We used data across nearly 40 years from a large-scale deforestation experiment in the central Amazon to examine the avian colonization process in a spatial and temporal framework, considering the role that roads may play in facilitating colonization.</p> <p>Since 1979, 139 species that are not part of the original forest avifauna have been recorded, including more secondary forest species than expected based on the regional species pool. Among the 35 species considered to have colonized and become established, a disproportionate number were secondary forest birds (63%), almost all of which first appeared during the 1980s. These new residents comprise about 13% of the current community of permanent residents.</p> <p><span>Widespread generalists associated with secondary forest colonized quickly following deforestation, with few new species added after the first decade, despite a stable road connection. Few species associated with riverine forest or specialized habitats colonized, despite road connection to their preferred source habitat. </span>Colonizing species remained restricted to anthropogenic habitats and did not infiltrate old-growth forests nor displace forest birds.</p> <p>Deforestation and expansion of road networks into <i>terra firme </i>rainforest will continue to create degraded anthropogenic habitat. Even so, the initial pulse of colonization by non-primary forest bird species was not the beginning of a protracted series of invasions in this study, and the process appears to be reversible by forest succession. </p>
Degradation corrected 0.05 degree GOME-2 SIF datasets in Amazon area
<p>An 8-day instrument degradation corrected 0.05 degree GOME-2 SIF dataset in Amazon area from 2010 to 2018. PK dataset from the spatially downscaled sun-induced fluorescence global product proposed by Gregory Duveiller in 2020 is corrected based on a pseudo-invariant method and then masked. Mean value composite method is used to produce monthly data. Files are organized in TIF format.</p>
L4D - Probability map of giant trees occurrence (> 70 m) in the Brazilian Amazon
<p>The probability of giant trees occurrence (> 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). </p>
L4B - Maximum tree height map for the Brazilian Amazon
<p>Maximum tree height distribution estimated by the Random Forest model based on the environmental variables. To explore the influence and importance of the environmental variables for development in tree height, we employed Random Forest modeling, which consists of generating a large number of regression trees, each constructed considering a random data subset. The regression trees are used to identify the best sequence to split the solution space to estimate the output. Were considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). </p>
Coronavirus prevalence in Brazilian Amazon and Sao Paulo city
<p>SARS-CoV-2 spread rapidly in the Brazilian Amazon. Mortality was elevated, despite the young population, with the health services and cemeteries overwhelmed. The attack rate in this region is an estimate of the final epidemic size in an unmitigated epidemic. Here we show that by June, one month after the epidemic peak in Manaus, capital of the Amazonas state, 44% of the population had detectable IgG antibodies. This equates to a cumulative incidence of 52% after correcting for the false-negative rate of the test. Further correcting for the effect of antibody waning we estimate that the final attack rate was 66%. This is higher than seen in other settings, but lower than the predicted final size for an unmitigated epidemic in a homogeneously mixed population. This discrepancy may be accounted for by population structure as well as some limited physical distancing and non-pharmaceutical measures adopted in the city.</p>
Demographic and growth rings data of Pentaclethra macrolona in the Amazon River estuary
<p>Little is known about the natural history of the hyperdominant Amazonian tree <em>Pentaclethra macroloba</em>. It has multiple uses and is widely explored, due to the important phytotherapy properties of the oil of its seeds. We determined the demographic and growth patterns of <em>P. macroloba</em> and analyzed the influence of the daily tide in its growth trajectory. We studied populations of <em>P. macroloba</em> in the APA and CEM, northeast of the Brazilian Amazon. In 136.59 ha of the APA, all adult trees with diameter ≥ 5 cm were quantified and two plots of 1 ha each were installed for sampling of the regeneration (diameter < 5 cm). Samples of the radial wood of 38 trees were obtained to determine age and growth rates, through dendrochronological analyzes. In CEM, exchange activity of 30 trees, at different topographic levels, was monitored by dendrometer bands. The effect of precipitation, temperature and flood on the exchange activity of <em>P. macroloba</em> was evaluated using multiple regressions. We model the growth of the species based on the widths of its growth rings. We invented 2,072 adult trees (15 individuals ha<sup>-1</sup>), distributed in 12 diametric classes (log-normal pattern), mean diameter of 23 cm, total basal area 98.13 m², height mean of 12.7 m and aggregate distribution pattern (R = 0.63, p <0.002). 240 regenerants (120 individuals ha-1) were quantified, distributed in nine diametric classes (negative exponential pattern), mean height of 0.63 m and aggregate pattern. Growth rings formed by marginal parenchyma show maximum age of 102 years and mean of 60 years for <em>P. macroloba</em>. The relationship between age and diameter was highly significant (r<sup>2</sup> = 0.98; p <0.001), as well as the relationship between height and diameter (r<sup>2</sup> = 0.79; p <0.001). The growth models show increment peaks in diameter and height at the age of 46 years (9.38 mm year<sup>-1</sup>) and 20 years (48.2 cm year<sup>-1</sup>) respectively. Greatest accumulation of biomass occurred at the age of 66 years (40.8 kg year<sup>-1</sup>). <em>P. macroloba</em> presents exchange dormancy in the rainy season (t = -2.62; p <0.01) and of river flooding (t = -3.01; p < 0.01). The existence of rings in <em>P. macroloba</em> is an important discovery, as only <em>Mora paraensis</em> had growth rings record in the Amazon estuary. The structural and growth patterns of <em>P. macroloba</em> are reflections of its life history and interactions with the environmental dynamics of estuarine floodplain.</p>
Amazon and Atlantic Forest image datasets for semantic segmentation
<p>This database contains images from<strong> Amazon </strong>and <strong>Atlantic Forest </strong>brazilian biomes used for training a fully convolutional neural network for the semantic segmentation of forested areas in images from the Sentinel-2 Level 2A Satellite.</p> <p>The images refer to the composition of bands 4, 3, 2 and 8. Each band was converted to a byte type (0-255).</p> <p>The images are still divided into three main sets: training, validation and testing:</p> <ol> <li><strong>Training dataset: </strong>it contains 499 and 485 GeoTIFF images (Amazon and Atlantic Forest, respectively) with 512x512 pixels and associated PNG masks (forest indicated in white and background in black color).</li> <li><strong>Validation dataset</strong>: it contains 100 GeoTIFF images for each biome with 512x512 pixels and associated PNG masks used for validation step.</li> <li><strong>Test dataset: </strong>it contains 20 GeoTIFF images for each biome with 512x512 pixels for testing.</li> </ol>
Data from: Deforestation risks posed by oil palm expansion in the Peruvian Amazon
Further expansion of agriculture in the tropics is likely to accelerate the loss of biodiversity. One crop of concern to conservation is African oil palm (Elaeis guineensis). We examined recent deforestation associated with oil palm in the Peruvian Amazon within the context of the region's other crops. We found more area under oil palm cultivation (845 km2 ) than did previous studies. While this comprises less than 4% of the cropland in the region, it accounted for 11% of the deforestation from agricultural expansion from 2007 to 2013. Patches of oil palm agriculture were larger and more spatially clustered than for other crops, potentially increasing their impact on local habitat fragmentation. Modeling deforestation risk for oil palm expansion using climatic and edaphic factors showed that sites at lower elevations, with higher precipitation, and lower slopes than those typically used for intensive agriculture are at long-term risk of deforestation from oil palm agriculture. Within areas at long-term risks, based on CART models, areas near urban centers, roads, and previously deforested areas are at greatest short-term risk of deforestation. Existing protected areas and officially recognized indigenous territories cover large areas at long-term risk of deforestation for oil palm (>40%). Less than 7% of these areas are under strict (IUCN I-IV) protection. Based on these findings, we suggest targeted monitoring for oil palm deforestation as well as strengthening and expanding protected areas to conserve specific habitats.
Fig. 10 in A new Centromochlus Kner, 1858 (Siluriformes: Auchenipteridae: Centromochlinae) from the transition between Amazon floodplain and Guiana shield, Brazil
Fig. 10. Distribution of Centromochlus orca. Green star represents type locality.
Fig. 9 in A new Centromochlus Kner, 1858 (Siluriformes: Auchenipteridae: Centromochlinae) from the transition between Amazon floodplain and Guiana shield, Brazil
Fig. 9. Centromochlus orca, new species, just after capture. Photo by H. Lazzarotto.
Figure 5 in Two new species of Passalus Fabricius (Coleoptera: Passalidae) from the western Brazilian Amazon with comments on the taxonomic limits of the subgenera
Figure 5. Passalus (Passalus) cleidecostae sp. nov. (A) head and prothorax, dorsal; (B) head, ventral (C) head and prothorax, dorsolateral; (D) posterior region of the prosternum and anterior region of the mesosternum; (E) metasternum (left side); (F) aedeagus (ventral, dorsal and lateral view).
Figure 6 in Two new species of Passalus Fabricius (Coleoptera: Passalidae) from the western Brazilian Amazon with comments on the taxonomic limits of the subgenera
Figure 6. Passalus (Passalus) cleidecostae sp. nov. (A) cephalic capsule, dorsal; (B) pronotum, lateral; (C) meso- and metasternum. Passalus (Passalus) bucki Luederwaldt, 1931: (D) cephalic capsule, dorsal v. (E) pronotum, lateral. (F) meso- and metasternum.
Figure 3 in Two new species of Passalus Fabricius (Coleoptera: Passalidae) from the western Brazilian Amazon with comments on the taxonomic limits of the subgenera
Figure 3. Passalus (Pertinax) deuterocerus sp. nov. (A) cephalic capsule, dorsal; (B) mentum; (C) meso- and metasternum. Passalus (Pertinax) epiphanoides (Kuwert, 1891): (D) cephalic capsule, dorsal. (E) mentum. (F) meso- and metasternum.
Figure 2 in Two new species of Passalus Fabricius (Coleoptera: Passalidae) from the western Brazilian Amazon with comments on the taxonomic limits of the subgenera
Figure 2. Passalus (Pertinax) deuterocerus sp. nov. (A) head and prothorax, dorsal; (B) head, ventral; (C) head and prothorax, dorsolateral; (D) posterior region of the prosternum and anterior region of the mesosternum; (E) metasternum (left side); (F) aedeagus (ventral, dorsal and lateral view).
Data from: Age‐dependent leaf physiology and consequences for crown‐scale carbon uptake during the dry season in an Amazon evergreen forest
* Satellite and tower-based metrics of forest-scale photosynthesis generally increase with dry season progression across central Amazônia, but the underlying mechanisms lack consensus. * We conducted demographic surveys of leaf age composition, and measured age-dependence of leaf physiology in broadleaf canopy trees of abundant species at a central eastern Amazon site. Using a novel leaf-to-branch scaling approach, we used this data to independently test the much-debated hypothesis—arising from satellite and tower-based observations—that leaf phenology could explain the forest-scale pattern of dry season photosynthesis. * Stomatal conductance and biochemical parameters of photosynthesis were higher for recently mature leaves than for old leaves. Most branches had multiple leaf age categories simultaneously present, and the number of recently mature leaves increased as the dry season progressed because old leaves were exchanged for new leaves. * These findings provide the first direct field evidence that branch-scale photosynthetic capacity increases during the dry season, with a magnitude consistent with increases in ecosystem-scale photosynthetic capacity derived from flux towers. Interaction between leaf age-dependent physiology and shifting leaf age-demographic composition are sufficient to explain the dry season photosynthetic capacity pattern at this site, and should be considered in vegetation models of tropical evergreen forests.
FIGURE 1. Hemiodus jatuarana n in Hemiodus jatuarana, a new species of Hemiodontidae from the rio Trombetas, Amazon Basin, Brazil (Teleostei, Characiformes)
FIGURE 1. Hemiodus jatuarana n. sp., holotype, rio Trombetas, Brazil, MZUSP 54083, 192 mm SL.
An Empirical Analysis of Amazon EC2 Spot Instance Features Affecting Cost-effective Resource Procurement
<p>This repository contains code and data for the paper "An Empirical Analysis of Amazon EC2 Spot Instance Features Affecting Cost-effective Resource Procurement", by Cheng Wang, Qianlin Liang and Bhuvan Urgaonkar.</p>
Code and tables for main figures of Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020
<p>Code and tables to reproduce the main figures (2a,2b,3a,3b,4a,5a) of Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020.</p> <p> </p>
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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.