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Central Amazon Basin water masks
<p>Water masks generated using a fixed threshold approach and Sentinel-1 images. <br><br>More information could be obtained in the paper. </p>
Tracking and classifying Amazon fire events in near-real time
<p><strong>Summary</strong></p> <p>Time-series (2018-2024) of the Amazon dashboard, including minor updates to the methods.</p> <p>The Amazon dashboard data product tracks individual fire events across most of South America (10N - 25S, 85W - 30W) in near-real time. The model classifies fires into four key fire types (deforestation, forest, small clearing and agricultural, and savanna and grassland fires) and provides estimates of individual fire carbon emissions. Methods are described in Andela et al. (2022). Near-real time estimates are provided at https://amzfire.servirglobal.net/ and here we archive historic time-series.</p> <p><strong>Methods</strong></p> <p>The data archived here (v1.1) include several small updates.</p> <p>Two updates relate to the use of VIIRS active fire detections. First, VIIRS active fire detections have been updated from collection 1 to collection 2. Second, any full day of missing data from either the VIIRS instrument onboard NOAA-20 or Suomi NPP is now replaced by data of the other instrument. This "gap" filling helps reduce the impact of periods with instrument outage, like those of Suomi NPP VIIRS during the 2024 burning season. </p> <p>The other two updates relate to the emissions calculations. First, to convert dry matter burned to carbon emissions, we have introduced fire type specific emissions factors instead of the earlier assumption of 50% carbon content for all fire types. Second, as part of the Sense4Fire project (https://sense4fire.eu/), we provide daily gridded emissions estimates of Dry Matter (DM), C, CO2, CO, and NOx at 0.1 degree resolution. We used emissions factors provided by Andrea (2019) for savanna and grassland fires as well as small clearing and agricultural fires while for forest and deforestation fires we reviewed the literature to select the most relevant emissions factors (Table 1). </p> <p>Table 1: Emissions factors (gram species per kg dry matter burned) used to calculate C, CO2, CO, and NOx emissions. </p> <table> <tbody> <tr> <td>Fire type / trace gas emissions</td> <td>C</td> <td>CO2</td> <td>CO</td> <td>NOx</td> </tr> <tr> <td>Savanna and grassland</td> <td>480</td> <td>1656</td> <td>69.2</td> <td>2.5</td> </tr> <tr> <td>Small clearing and agricultural</td> <td>430</td> <td>1431</td> <td>76.2</td> <td>2.4</td> </tr> <tr> <td>Forest</td> <td>480</td> <td>1561</td> <td>104.0</td> <td>2.0</td> </tr> <tr> <td>Deforestation</td> <td>490</td> <td>1641</td> <td>95.5</td> <td>1.7</td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset description<br></strong></p> <p>For full detail, please see Andela et al. (2022). The tables below (Tables 2 - 4) describe the content of the fire event (polygon) and active fire detections (point) shapefiles as well as the gridded emissions product. The active fire detections and associated estimates of dry matter burned can be combined with emissions factors (Table 1) to derive daily trace gas emissions time series for species and areas of interest.</p> <p>Table 2: Explanation of fire event shapefile attribute table.</p> <table> <tbody> <tr> <td>Attribute class</td> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>Fire type classification</td> <td>Fire type</td> <td>(1) savanna and grassland, (2) small clearing and<br>agriculture, (3) forest, and (4) deforestation fires</td> </tr> <tr> <td> </td> <td>Confidence</td> <td>(1) low, (2) moderate, and (3) high</td> </tr> <tr> <td>Fire Atlas</td> <td>Size</td> <td>Fire size in km2</td> </tr> <tr> <td> </td> <td>Start day</td> <td>Day of new fire start as day of year (1-366)</td> </tr> <tr> <td> </td> <td>Duration</td> <td>Fire duration in days</td> </tr> <tr> <td> </td> <td>C Emissions</td> <td>Fire carbon emissions (ton C)</td> </tr> <tr> <td>Fire characterization</td> <td>Tree cover</td> <td>Average tree cover fraction within perimeter (%)</td> </tr> <tr> <td> </td> <td>Biomass</td> <td>Average biomass within fire perimeter (ton ha-1)</td> </tr> <tr> <td> </td> <td>Deforestation </td> <td>Fraction of 550 m grid cells with historic<br>deforestation (five years prior to fire) within fire perimeter (%)</td> </tr> <tr> <td> </td> <td>FRP</td> <td>Average fire radiative power (FRP) for all fire<br>detections within fire perimeter (MW)</td> </tr> <tr> <td> </td> <td>Persistence</td> <td>Average fire persistence across 550 m grid cells<br>within fire perimeter (days)</td> </tr> <tr> <td> </td> <td>Progression</td> <td>Average fire progression fraction across 550 m<br>grid cells within perimeter (%)</td> </tr> <tr> <td> </td> <td>Daytime</td> <td>Fraction of 1:30 pm detections (%) for all fire<br>detections within fire perimeter</td> </tr> <tr> <td> </td> <td>Detections</td> <td>Total active fire detections within fire perimeter</td> </tr> </tbody> </table> <p> </p> <p>Table 3: Explanation of active fire detection shapefile attribute table.</p> <table> <tbody> <tr> <td>Attribute class</td> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>VIIRS active fire detections</td> <td>FRP</td> <td>Fire radiative power (MW)</td> </tr> <tr> <td> </td> <td>DOY</td> <td>Day of year (1-366)</td> </tr> <tr> <td>Fire type classification</td> <td>Fire type</td> <td>(1) savanna and grassland, (2) small clearing and agriculture, (3) forest, and (4) deforestation fires</td> </tr> <tr> <td> </td> <td>Confidence</td> <td>(1) low, (2) moderate, and (3) high</td> </tr> <tr> <td>Emissions</td> <td>C Emissions</td> <td>Fire carbon emissions (ton C) associated with each active fire detection</td> </tr> <tr> <td> </td> <td>DM Emissions</td> <td>Dry matter burned (ton) associated with each active fire detection</td> </tr> </tbody> </table> <p> </p> <p>Table 4: Content of daily gridded (0.1 degree resolution) emissions netcdf files. The daily emissions product provides emissions estimates of dry matter, C, CO2, CO, and NOx. For DM and CO partitioned emissions are also provided by fire type, for other species these can be derived by multiplying the dry matter burned (DM) estimates with trace gas specific emissions factors (Table 1). Values of each grid cell can be multiplied by the number of seconds per day and grid cell area to calculate total emissions (convert "kg species m-2 s-1" to "kg species day-1 per grid cell").</p> <table> <tbody> <tr> <td>/ancill</td> <td>grid_cell_area</td> </tr> <tr> <td>/partitioned_DM_emissions</td> <td>Deforestation emissions</td> </tr> <tr> <td> </td> <td>Forest emissions</td> </tr> <tr> <td> </td> <td>Savanna and grassland emissions</td> </tr> <tr> <td> </td> <td>Small clearing and agricultural emissions</td> </tr> <tr> <td>/partitioned_CO_emissions</td> <td>Deforestation emissions</td> </tr> <tr> <td> </td> <td>Forest emissions</td> </tr> <tr> <td> </td> <td>Savanna and grassland emissions</td> </tr> <tr> <td> </td> <td>Small clearing and agricultural emissions</td> </tr> <tr> <td>/total_emissions</td> <td>DM emissions</td> </tr> <tr> <td> </td> <td>C emissions</td> </tr> <tr> <td> </td> <td>CO2 emissions</td> </tr> <tr> <td> </td> <td>CO emissions</td> </tr> <tr> <td> </td> <td>NOx emissions</td> </tr> </tbody> </table> <p> </p> <p><strong>Results</strong></p> <p>Despite the various small improvements to the dataset, the data are largely consistent with the original dataset published for 2019-2020 (Table 5). </p> <p>Table 5: Comparison of model versions (original from Andela et al., 2022 and v1.1 published here) for April-December 2019 (equator-25S, 85W - 30W). Note that the current version (v1.1) is complete for 2019, but the original dataset had missing data due to incomplete active fire detections from NOAA-20 VIIRS at that time.</p> <table> <tbody> <tr> <td>Dataset</td> <td>Fire type</td> <td>Fire detections (x1,000)</td> <td>Mean fire radiative power (MW)</td> <td>Number of events (x1,000)</td> <td>Emissions (Tg C)</td> </tr> <tr> <td>Original</td> <td>Deforestation</td> <td>756.65</td> <td>15.15</td> <td>24.24</td> <td>99.18</td> </tr> <tr> <td>Original</td> <td>Forest</td> <td>637.58</td> <td>12.73</td> <td>5.28</td> <td>85.46</td> </tr> <tr> <td>Original</td> <td>Small clearing and agricultural</td> <td>348.49</td> <td>10.91</td> <td>154.68</td> <td>10.55</td> </tr> <tr> <td>Original</td> <td>Savanna and grassland</td> <td>1935.06</td> <td>12.11</td> <td>296.42</td> <td>71.75</td> </tr> <tr> <td>v1.1</td> <td>Deforestation</td> <td>742.64</td> <td>14.81</td> <td>24.02</td> <td>97.18</td> </tr> <tr> <td>v1.1</td> <td>Forest</td> <td>626.92</td> <td>12.06</td> <td>5.16</td> <td>77.84</td> </tr> <tr> <td>v1.1</td> <td>Small clearing and agricultural</td> <td>350.7</td> <td>10.89</td> <td>155.56</td> <td>9.27</td> </tr> <tr> <td>v1.1</td> <td>Savanna and grassland</td> <td>1877.16</td> <td>11.89</td> <td>299.17</td> <td>70.55</td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The Sense4Fire project is funded by ESA under ESA Contract Number: 4000134840/21/I-NB. </p> <p><strong>References</strong></p> <p>Andela, N., Morton, D.C., Schroeder, W., Chen, Y., Brando, P.M. and Randerson, J.T., 2022. Tracking and classifying Amazon fire events in near real time. Science advances, 8, eabd2713. https://doi.org/10.1126/sciadv.abd2713.</p> <p>Andreae, M.O., 2019. Emission of trace gases and aerosols from biomass burning–an updated assessment. Atmospheric Chemistry and Physics, 19, 8523-8546. https://doi.org/10.5194/acp-19-8523-2019.</p>
Fig. 1 in Mercury bioaccumulation in fish of commercial importance from different trophic categories in an Amazon floodplain lake
Fig. 1. Map of lago Grande de Mancapuru.
Haplocauda, a new genus of fireflies endemic to the Amazon Rainforest (Coleoptera: Lampyridae)
<p>Most firefly genera have poorly defined taxonomic boundaries, especially in the Neotropics, where they are more diverse and more difficult to identify. Recent advances that shed light on the diversity of fireflies in South America have focused mainly on Atlantic Rainforest taxa, whereas lampyrids in other biomes remained largely unstudied. We found three new firefly species endemic to the Amazon basin that share unique traits of the male abdomen where sternum VIII and the pygidium are modified and likely work as a copulation clamp. Here we test and confirm the hypothesis that these three species form a monophyletic lineage and propose Haplocauda gen. nov. to accommodate the three new species. Both Maximum Parsimony and probabilistic (Bayesian and Maximum Likelihood) phylogenetic analyses confirmed Haplocauda gen. nov. monophyly, and consistently recovered it as the sister group to Scissicauda, fireflies endemic to the Atlantic Rainforest that also feature a copulation clamp on abdominal segment VIII, although with a different configuration. We provide illustrations, diagnostic descriptions, and keys to species based on males and females. The three new species were sampled from different regions, and are likely allopatric, a common pattern among Amazonian taxa.</p>
Rhinella_major_diet_eastern_Amazon
<p>We studied the diet of <em>Rhinella major </em>from temporary lentic water bodies from an urban area in Amapá state, North Brazil. A total of 59 frogs were manually collected between January and May 2019 (rainy season) and analysed using a stomach flushing method. Of these, 42 stomachs revealed 1,236 prey items, of which 1,059 (85.7%) were ants and 177 (14.3%) were other taxa. Isoptera and Coleoptera were the most abundant non-ant taxa. We found a high diversity of ants in the diet (11 genera from five subfamilies), Myrmicinae being the most abundant and frequent ant subfamily, mostly represented by the genera <em>Pheidole </em>and <em>Solenopsis.</em></p>
Amazon_Laptops_Joan
<p>The data set is made up of two columns “Name_of_Product” and “Value”, which contain information about the name of the laptop + the price you get. The details have been retrieved directly from the Amazon website searching for the paraula "laptops", both of which the time is due (darrera vegada que s'ha execuat el code) on 04/11/2021.</p>
Grain size distribution of Amazon river sediment samples collected over the period 2005-2008; and ADCP water velocity profiles collected on the major tributaries of the Amazon in Bolivia and Peru, 2007-2008
<p>This dataset contains two items:</p> <p>- The grain size distribution of river sediment samples collected along the Amazon River and its tributaries during four sampling campaigns performed in June 2005 (lower Amazon, Brazil), March 2006 (lower Amazon, Brazil), May 2007 (Upper Madeira, Bolivia), and April 2008 (Upper Solimões-Amazonas, Peru). [spreadsheet "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_data.xlsx"].</p> <p>- River water velocity profiles derived from Acoustic Doppler Current Profiler (ADCP) measurements performed on the major tributaries of the Amazon in May 2007 (Upper Madeira, Bolivia) and April 2008 (Upper Solimões-Amazonas, Peru) [folder "ADCP_dataset_Amazon_2007-2008_Bouchez_data"].</p> <p>The dataset description and the relevant references are provided in the text files "Grain_size_distribution_dataset_Amazon_2005-2008_Bouchez_description.docx" and "ADCP_dataset_Amazon_2007-2008_Bouchez_description.docx" .</p> <p>These data were acquired thanks to the support of the French National Service for Observation "HYBAM" ("Hydrogeochemistry of the Amazon Basin"), part of the CNRS National Infrastructure "OZCAR" ("Critical Zone Observatories: Applications and Research").</p>
Linking land-use and land-cover transitions to their ecological impact in the Amazon
<p>Authors: Cássio Alencar Nunes, Erika Berenguer, Filipe França, Joice Ferreira, Alexander C. Lees, Julio Louzada, Emma J. Sayer, Ricardo Solar, Charlotte C. Smith, Luiz E. O. C. Aragão, Danielle de Lima Braga, Plinio Camargo, Carlos Eduardo Pellegrino Cerri, Raimundo Cosme, Mariana Durigan, Nárgila Moura, Victor Hugo Fonseca Oliveira, Carla Ribas, Fernando Vaz-de-Mello, Ima Vieira, Ronald Zanetti, Jos Barlow</p> <p>Code repository for the paper: Nunes et al. Linking land-use and land-cover transitions to their ecological in the Amazon. Proceedings of the National Academy of Sciences. 2022. In this repository we included codes and data that we used to run the all the analyses presented in the paper.</p>
Enviromental DNA datasets of the Colombian Amazon and Orinoco basins
<p><span>The massive loss of biodiversity in recent years has driven the development of rapid, cost-effective, non-invasive, and efficient sampling alternatives, such as environmental DNA. With this method, a water sample can be used to evaluate a community's diversity, in addition with low abundance, cryptic and threatened species detection. Therefore, in this study, environmental DNA was used to determine the diversity of aquatic, semi-aquatic and terrestrial vertebrates in the Colombian Amazon and Orinoco basins, which included four main subregions: Bojonawi Natural Reserve and adjacent areas (Vichada Department), Sierra de la Macarena National Park and Tillavá (Meta Department), Puerto Nariño and adjacent areas (Amazonas Department) and the Municipality of Solano (Caquetá Department). A total of 709 OTUs were identified for all locations. The Orinoco river showed the highest number of fish genera (68) and the Guayabero river, the largest number of genera for tetrapods (13). New taxonomic records were found on all locations, mianly in Bita, Orinoco and Tillavá rivers, which portrayed the highest record of unknown fish diversity compared with traditional surveys. Likewise, two vulnerable fish species and three vulnerable mammal species were identified, as well as four threatened mammal species, including the giant otter (<em>Pteronura brasiliensis</em>), the giant anteater (<em>Myrmecophaga tridactyla</em>), the two subspecies of the Amazon river dolphin (<em>Inia geoffrensis geoffrensis</em> and <em>Inia geoffrensis humboldtiana</em>) and the tucuxi (<em>Sotalia fluviatilis</em>). It is essential to improve current DNA sequence databases for the neotropics and standardize the methodology according to the animal of interest in order to develop future studies that maximize environmental DNA analyses efficiency.</span></p>
A large-scale assessment of ant diversity across the Brazilian Amazon Basin: integrating geographic, ecological, and morphological drivers of sampling bias
<p>Tropical ecosystems are often biodiversity hotspots, and invertebrates represent the main underrepresented component of diversity in large-scale analyses. This problem is partly related to the scarcity of data widely available to conduct these studies and the lack of systematic organization of knowledge about invertebrates' distributions in biodiversity hotspots. Here, we introduce and analyze a comprehensive data compilation of Amazonian ant diversity. Using records from 1817 to 2020 from both published and unpublished sources, we describe the diversity and distribution of ant species in the Brazilian Amazon Basin. Further, using high-definition images and data from taxonomic publications, we build a comprehensive database of morphological traits for the ant species that occur in the region. In total, we recorded 1,067 nominal species in the Brazilian Amazon Basin, with sampling locations strongly biased by access routes, urban centers, research institutions, and major infrastructure projects. Large areas where ant sampling is non-existent represent about 52% of the basin and are concentrated mainly in the North, Southeastern, and Western Brazilian Amazon. We found that distance to roads is the main driver of ant sampling in the Amazon. Contrary to our expectations, morphological traits had lower predictive power in predicting sample bias than purely geographic variables. However, when geographic predictors were controlled, habitat stratum and traits contribute to explain the remaining variance. More species were recorded in better-sampled areas, but species richness estimation models suggest that areas in South Amazonian edge forests are associated with especially high species richness. Our results represent the first trait-based, large-scale study for insects in Amazonian forests and a starting point for macroecological studies focusing on insect diversity in the Amazon Basin.</p>
Data accessibility for conservation implications of genetic structure in the narrowest endemic quillwort from the Eastern Amazon
<p>The quillwort <em>Isoetes cangae</em> is a critically endangered species occurring in a single lake in Serra dos Carajás, Eastern Amazon. Low genetic diversity and small effective population sizes (N<sub>e</sub>) is expected for narrow endemic species (NES). Here, we evaluated genetic diversity, population structure, and N<sub>e</sub> of <em>I. cangae </em>to provide information for conservation programs. Conservation biology studies centered in a single‐species show some limitations but they are still useful considering the limited time and resources available for the protection of species at risk of extinction. Our analyses were based on 55 individuals collected from the Amendoim lake and 35,638 neutral SNPs. Our results indicated a single panmictic population, moderate levels of genetic diversity, and effective population size (N<sub>e</sub>) in the order of thousands, contrasting the expected for NES. Negative FIS values were also found, suggesting that <em>I. cangae</em> is not under risk of inbreeding depression. Our findings imply that <em>I. cangae </em>contains enough genetic diversity to ensure evolutionary potential, all individuals should be treated as one demographic unit.</p>
Effects of semi-constant temperature on embryonic and hatchling phenotypes of six-tubercled Amazon River turtles, Podocnemis sextuberculata
<p><strong>Purpose:</strong> We evaluated how constant incubation temperatures affect life-history traits pre-hatching and post-hatching of the six-tubercled Amazon River turtle, <em>Podocnemis sextuberculata</em>.</p> <p><strong>Methods:</strong> We incubated eggs from natural nests at ten semi-constant temperatures between 22.26±1.01°C and 37.37±0.38°C (2013) and at six temperatures between 25.75±0.22°C and 36.17±0.15°C (2016). In 2013, we raised hatchling for 90 days to evaluate effects of temperature on early hatchling growth. We evaluated maternal effects in 2016.</p> <p><strong>Results:</strong> <em>P. sextuberculata</em> displays temperature-dependent sex determination and produces males at colder and females at warmer temperatures (TSD Ia). The estimated pivotal temperature was 33.73 ± 0.15°C and the transitional range of temperatures (TRT) 1.16 ± 0.59°C. Semi-constant temperatures below 26°C and above 38°C were lethal. Intermediate temperatures (32.25°C and 31.5°C, respectively) were optimal for hatching success and produced larger hatchlings that grew slower early in life compared to colder or warmer conditions, which produced smaller hatchlings. Warmer incubation temperatures within the optimal range (28°C-37°C) accelerated embryonic development. In contrast, comparisons of 30, 60 and 90 days-old suggests that warmer incubation temperatures reduced growth and mass gain rates post-hatching, such that incubation temperature effects on body size at emergence disappeared by 3 months of age.</p> <p><strong>Conclusions</strong>: Six-tubercled Amazon River turtles showed the highest pivotal temperature reported for any turtle. The relatively narrow TRT may limit the evolutionary potential of this vulnerable turtle in the face of global warming. Future incubation experiments at a finer scale (33°C-36°C) are warranted to refine the sex-ratio reaction norm. Field studies that monitor natural nests are imperative to evaluate conservation measures and the effect of female-biased illegal hunting and climate change. By providing data about the thermal biology of an understudied lineage of non-model species, our study helps fill gaps in our understanding of the evolution of vertebrate sex determination and its potential adaptive value.</p>
Dataset: Local hydrological conditions influence tree diversity and composition across the Amazon basin
<p>Tree diversity and composition in Amazonia are known to be strongly determined by the water supplied by precipitation. Nevertheless, within the same climatic regime, water availability is modulated by local topography and soil characteristics (hereafter referred to as local hydrological conditions), varying from saturated and poorly drained to well-drained and potentially dry areas. While these conditions may be expected to influence species distribution, the impacts of local hydrological conditions on tree diversity and composition remain poorly understood at the whole Amazon basin scale. Using a dataset of 443 1-ha non-flooded forest plots distributed across the basin, we investigate how local hydrological conditions influence 1) tree alpha diversity, 2) the community-weighted wood density mean (CWM-wd) – a proxy for hydraulic resistance, and 3) tree species composition. We find that the effect of local hydrological conditions on tree diversity depends on climate, being more evident in wetter forests, where diversity increases towards locations with well-drained soils. CWM-wd increased toward better-drained soils in Southern and Western Amazonia. Tree species composition changed along local soil hydrological gradients in Central-Eastern, Western and Southern Amazonia, and those changes were correlated with changes in the mean wood density of plots. Our results suggest that local hydrological gradients filter species, influencing the diversity and composition of Amazonian forests. Overall, this study shows that the effect of local hydrological conditions is pervasive, extending overwide Amazonian regions, and reinforces the importance of accounting for local topography and hydrology to better understand the likely response and resilience of forests to increased frequency of extreme climate events and rising temperatures.</p>
Dominant Rural Technological Trajectories (TTs) dataset at municipality level of the Brazilian Legal Amazon (BLA)
<p>This dataset contains the dominant technological trajectories (TTs) of the Brazilian Legal Amazon (BLA) 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 (2022), 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> <p>Costa, F. A, (2022). Database of Rural Technological Trajectories of the Legal Amazon delimited by the Method of Differentiation and Structural Signification of Rural Production (M-DASTRU). <strong>Zenodo.</strong> DOI: 10.5281/zenodo.7035753</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 4 in New records and a voucher collection of parasitoid wasps (Hymenoptera) inhabiting agroforestry systems in the colombian amazon basin
Figure 4. Habitus of Chalcidoidea collected in agroforestry systems of cacao (Theobroma cacao) and copoazu (T. grandiflorum). (A) Haltichella hydara (Walker, 1842) [UNAB 1190], (B) Stypiura sp. [UNAB 1356], (C) Aenasius sp. (Encyrtidae) [UNAB 663], (D) Coelopencyrtus sp. [UNAB 2538], (E) Horismenus striatus Hansson, 2009 (Eulophidae) [UNAB 1196], (F) Brasema sp. (Eupelmidae) [UNAB 1358], (G) Anastatus sp. (Eupelmidae) [UNAB 1192], (H) Neorileya flavipes Ashmead, 1904 (Eurytomidae) [UNAB 1432], (I) Perilampus sp. (Perilampidae) [UNAB 1361], (J) Erotolepsia sp. (Pteromalidae) [UNAB 661], (K) Bubekia tricarinata (Ashmead, 1888) (Pteromalidae) [UNAB 3464], (L) Lelaps affinis Ashmead, 1904 (Pteromalidae) [UNAB 658].
Figure 3 in New records and a voucher collection of parasitoid wasps (Hymenoptera) inhabiting agroforestry systems in the colombian amazon basin
Figure 3. Habitus of parasitoid wasps collected in agroforestry systems of cacao (Theobroma cacao) and copoazu (T. grandiflorum). (A) Aclista sp. (Diapriidae) [UNAB 3747], (B) Basalys sp. (Diapriidae) [UNAB 1198], (C) Paramesius sp. (Diapriidae) [UNAB 1194], (D) Trichoplasta sp. (Figitidae) [UNAB 1423], (E) Aganaspis sp. (Figitidae) [UNAB 1464], (F) Tropideucoila sp. (Figitidae) [UNAB 1422].
Figure 2 in New records and a voucher collection of parasitoid wasps (Hymenoptera) inhabiting agroforestry systems in the colombian amazon basin
Figure 2. Bar charts of parasitoid wasp families and genera of collected individuals inhabiting agroforestry systems of cacao (Theobroma cacao) and copoazu (T. grandiflorum) from the Colombian Amazon basin. (A) number of individuals per family, (B) number of genera and species per family.
Figure 5 in New records and a voucher collection of parasitoid wasps (Hymenoptera) inhabiting agroforestry systems in the colombian amazon basin
Figure 5. Habitus of Platygastroidea (Scelionidae) collected in agroforestry systems of cacao (Theobroma cacao) and copoazu (T. grandiflorum). (A) Apegus sp. [UNAB 1445], (B) Calliscelio sp. [UNAB 1439], (C) Gryon sp. [UNAB 1442], (D) Scelio sp. [UNAB 1438], (E) Xenomerus sp. [UNAB 1443], (F) Thoron sp. [UNAB 3765].
Raster-based dataset for spatio-temporal analysis of forest fires in the Amazon rainforest from 2001 to 2020
<p>Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associated study to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides three sets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world. </p>
ScienceDex guides
Understand access before you commit
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.