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FIG. 2. — Ptiloneura leonardoi n in The genus Ptiloneura Enderlein, 1901 (Psocodea, 'Psocoptera', Ptiloneuridae) in the Brazilian Amazon Forest and Atlantic Forest: new species, variations in forewings and a key to the species
FIG. 2. — Ptiloneura leonardoi n. sp. holotype: A, front view of head; B, right forewing; C, right hindwing; D, left forewing; E, left hindwing; F, phallosome; G, hypandrium; H, left paraproct and epiproct. Abbreviations: aes, anterior endophallic sclerites; ep, external parameres; les, lateral endophallic sclerites; ms, mesal sclerite; msp, mesal sclerite processes; pes, posterior endophallic sclerites; st, side struts. Scale bars: A, F-H, 0.1 mm; B-E, 1 mm.
Conversion from forest to agriculture in the Brazilian Amazon from 1985 to 2021
<p>This file collection contains data with conversion length from forests to agriculture in the Brazilian Amazon, from 1985 to 2021. Calculations were based in the MapBiomas thematic maps. More information can be found in the repository website (<a href="https://github.com/hugotseixas/forest-agri-conversion/tree/3.0.0">https://github.com/hugotseixas/forest-agri-conversion/tree/3.0.0</a>).</p> <p>The collection is composed of six sets of data:</p> <p><strong>c_raster_mosaic</strong>: raster files of the Amazon biome with values of conversions;</p> <p><strong>c_raster_tiles</strong>: raster files of smaller raster tiles, with values of conversions;</p> <p><strong>c_tabular_dataset</strong>: a group of tables that contains data about conversions;</p> <p><strong>figures</strong>: data to create figures of the manuscript;</p> <p><strong>raw_raster_tiles</strong>: raster files of smaller raster tiles with land use and land cover classification data from MapBiomas;</p> <p><strong>validation</strong>: data used to performed validation of the conversion results.</p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Mato Grosso, Amazonas e Pará)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Mato Grosso (1 zip file), Amazonas (2 zip files) and Pará (8 zip files).</p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Roraima e Amapá)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Amapá (1 zip file) and Roraima (1 zip file).</p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Acre e Rondônia)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Acre (1 zip file) and Rondônia (1 zip file).</p>
Figure 4 in Morphological and Molecular Characteristics of Kudoa viseuensis n. sp. (Myxosporea: Multivalvulida), Found in the Muscle of Batrachoides surinamensis (Teleostei: Batrachoididae) in the Brazilian Amazon Region
Figure 4. Phylogenetic tree derived from Bayesian Inference (BI), based on the partial sequences of the SSU rDNA gene of Kudoa vi- seuensis n. sp. and closely-related myxosporans. The GenBank access numbers are shown next to the species names, and the numbers at each node are the BI posterior probabilities. The new species is highlighted in bold type. Abbreviations: Msl – muscle; Dtr – digestive tract; Nsy – nervous system.
Figure 2 in Morphological and Molecular Characteristics of Kudoa viseuensis n. sp. (Myxosporea: Multivalvulida), Found in the Muscle of Batrachoides surinamensis (Teleostei: Batrachoididae) in the Brazilian Amazon Region
Figure 2. Light photomicrograph: (A) longitudinal histology section of the skeletal musculature of B. surinamensis containing a pseudocyst (*), along the axis of the muscle, showing the substitution of the fiber by the parasite; (B) transversal section showing the pseudocyst of the mixosporean occupying the central portion of the muscle fiber (arrowhead), typical of an individual infection; (C) Multiple infection of pseudocysts within a single muscle fiber, separated from one another and the muscle tissue by a fine conjunctive membrane (arrows). Scale bars: 40 µm.
Figure 1 in Morphological and Molecular Characteristics of Kudoa viseuensis n. sp. (Myxosporea: Multivalvulida), Found in the Muscle of Batrachoides surinamensis (Teleostei: Batrachoididae) in the Brazilian Amazon Region
Figure 1. Light photomicrograph: (A) Whitish pseudocyst (arrowhead) found in the musculature of B. surinamensis. Scale bar: 1000 µm; (B) pseudocyst (c) and numerous mature spores (e) observed following the rupture of the pseudocyst. Scale bar: 100 µm; (C) Fresh, pseu- do-square spores (e) of Kudoa viseuensis n. sp. Scale bar: 20 µm; Inset: polar capsules (PC) in lateral (L) and apical (A) views (DIC). Scale bar: 10 µm.
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>
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).
Figures 15-18 in New species of Sycorax (Diptera: Psychodidae) from the Brazilian Amazon
Figures 15-18. Sycorax manauara sp. nov., female. (15) Head, anterior view. (16) Wing. (17) Apex of abdomen, ventral view. (18) Genital fork (white arrow) and sphermathecal ducts (black arrow).
Figures 10-14 in New species of Sycorax (Diptera: Psychodidae) from the Brazilian Amazon
Figures 10-14. Sycorax manauara sp. nov., male Terminalia. (10) Lateral view. (11) Dorsal view. (12) Ventral view. (13) Detailed drawing of lateral view. (14) Detail of aedeagal complex. Abbreviations: ae = aedeagus; ce = cercus; ea = ejaculatory apodeme; ep = epandrium; gc = gonocoxite; gn = gonostyle; ipr = internal branch of paramere; pb = parameral bridge; pr = paramere.
Figures 1-5 in New species of Sycorax (Diptera: Psychodidae) from the Brazilian Amazon
Figures 1-5. Sycorax manauara sp. nov., male head. (1) Anterior view. (2) Posterior view. (3) Head and complete antennae. (4) Detail of flagellomeres 1-3 with ascoids. (5) Detail of apical flagellomeres.
Data from: The latest freshwater giants: a new Peltocephalus (Pleurodira: Podocnemididae) turtle from the Late Pleistocene of the Brazilian Amazon
<p>Overkill of large mammals is recognised as a key driver of Pleistocene megafaunal extinctions in the Americas and Australia. While this phenomenon primarily affected mega-mammals, its impact on large Quaternary reptiles has been debated. Freshwater turtles, due to the scarcity of giant forms in the Quaternary record, have been largely neglected in such discussions. Here we present a new giant podocnemidid turtle, <em>Peltocephalus maturin</em> sp. nov., from the Late Pleistocene Rio Madeira Formation in the Brazilian Amazon, that challenges this assumption. Morphological and phylogenetic analyses of the holotype, a massive partial lower jaw, reveal close affinities to extant Amazonian species and suggests an omnivorous diet. Body size regressions indicate <em>Pe. maturin</em> possibly reached about 180 cm in carapace length and is amongst the largest freshwater turtles ever found. This finding presents the latest known occurrence of giant freshwater turtles, hinting at coexistence with early human inhabitants in the Amazon.</p>
Data from: Dung beetles from three primary forest sites in the Brazilian Amazon
<p><em>Scarabaeinae</em> beetles (Coleoptera: Scarabaeidae) is a high performance indicator group used in biodiversity assessment of tropical forests. As most species are coprophagous they are commonly known as dung beetles although few species may be necrophagous or saprophagous. Allied to the feeding habitat dung beetles display a complex reproductive behaviour with nests underground, and therefore act in many ecossistem services as nutrient cycling, bioturbation and secondary seed dispersal. Dung beetles display specific forraging methods, as they detect food odors through the antennas and fly towards the food resource. They are therefore captured by baited pitfall and flight interception traps, and studies have shown the complementary between these methods. However, the use of attractants can introduce additional sources of variation in trap performance. In our paper we aimed to assess the effect of bait attractiveness on assemblages metrics when sampled by baited pitfall traps. We identified species less and highly attracted to the baits in use, and that assemblages sampled with baited traps tend to lower diversity and higher dominance.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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