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3,959 results for “Ecuador”
InSAR stack of Fernandina volcano in Galápagos, Ecuador from Sentinel-1 descending track 128 processed with ISCE2/topsStack
<p>A stack of unwrapped interferograms on Fernandina volcano, Galápagos, Ecuador</p> <p>Sensor: Sentinel-1descending track 128</p> <p>Processor: ISCE/topsStack</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p> <p><strong>Version 1.x (~750 MB)</strong><br> Time: 2014.12.13 - 2018.06.19 (98 acquisitions, 288 interferograms)</p> <p><strong>Version 0.1 (~280 MB; for fast testing of code development)</strong><br> Time: 2014.12.13 - 2016.05..24 (36 acquisitions, 102 interferograms)</p>
Hydrochemical Data from a Tropical Andean Glacierized Catchment: δ18O, electrical conductivity, maximum fluorescence intensity, and dissolved organic carbon concentrations from short-term sampling campaigns, Ecuador (2022 and 2024)
Fluorescent dissolved organic matter (FDOM) quality, dissolved organic carbon (DOC) concentration, electrical conductivity (EC), and stable water isotopes (δ¹⁸O and δ2H) were determined in water, snow, and ice samples from a tropical glacierized catchment in the Ecuadorian Andes. The sampling locations were selected to capture the major hydrologic inputs to the main stream channel (glacial melt, tributaries, wetlands, and groundwater springs) and constrain the in-stream spatiotemporal variation in DOM quality and other hydrochemical characteristics. Two sets of high-resolution time series were collected on Oct 13, 2022 and Jun 14, 2024. Time series samples were collected at various upper catchment locations and the outlet simultaneously. DOM quality was characterized via fluorescence spectroscopy and processed using parallel factor analysis (PARAFAC). The DOM quality data are expressed as %FMax values obtained through a 4-component PARAFAC model, where %Fmax 1– 4 are interpreted as terrestrial humic-like, tyrosine-like, tryptophan-like, and microbial humic-like fluorescent components, respectively. DOC concentrations were quantified using high-temperature catalytic combustion, stable water isotopes were analyzed using laser-based spectroscopy, and EC was measured in situ with handheld multiparameter water quality probes.
Data for manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)'
<p>The uploaded zip-file entails the data obtained through four field campaigns performed in the province of Azuay (Ecuador) in the period July 2023 - May 2024, which is used as a basis for the manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)' that was submitted to a Special Issue in the journal Water in 2024. The study aimed at illustrating the impact of urbanisation and drought on the abiotic water conditions of the rivers passing through the studied urban areas.</p> <p>The data includes a subfolder with data obtained from an external website (https://generacioncsr.celec.gob.ec/graficasproduccion/) and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (to be added when the manuscript is accepted). The data file only contains the baseline data, while results can be obtained through running the R-scripts in the GitHub-repository. Additional comments on the analyses are also provided in the analysis scripts.</p> <p><strong>DATA COLLECTION</strong></p> <p>Information on the locations was collected prior to the first field campaign (July 2023) and confirmed in the field (and corrected when necessary). The following variables were registered: Date & Time, Coordinates (latitude and longitude, in WGS84 format), Altitude (in meters above mean sea level), Distance (to a fixed location downstream; being the province border), and Category (River or Stream).</p> <p>Information on the physicochemical conditions was collected directly in the field with a <strong>Horiba U-52</strong> multiprobe. The following variables were registered: Temperature, pH, Electrical conductivity (reference at 25 °C), Oxygen level (as concentration), and Turbidity (in NTU).</p> <p>At each site, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. The multiprobe was rinsed with this sample water and then submerged in the bucket, followed by continuous stirring (to avoid a decrease of the oxygen levels) until the readings stabilised. After stabilisation, readings were recorded on a separate data sheet prior to being digitalised.</p> <p>Information on the nutrient levels was obtained through the collection of water samples in the field and the subsequent analysis in the laboratory. The following nutrients were selected: ammonium, nitrate, nitrite, and orthophosphate. For the analyses, <strong>Merck test kits</strong> (equivalent to USEPA analyses) were used in combination with a Genesys UV-VIS spectrophotometer (Thermofisher).</p> <p><strong>In the field</strong>, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. A polyethylene syringe was rinsed thrice with sample water and subsequently filled prior to being fitted with a 0.45 µm PES filter. About 100 mL of sampled water was filtered and collected in a 250 mL polyethylene bottle that was rinsed with the first 5 mL of filtered water. The bottle was stored in a cooling box and transported to the laboratory.</p> <p><strong>In the laboratory</strong>, the 250 mL bottle was stored at 4 °C until analysis. Within 36 hours, concentrations of ammonium, nitrate, nitrite, and orthophosphate were determined <strong>in triplicate</strong>. More specifically, the following test kits were used to determine said nitrogen and phosphorus concentrations (with quantification range between brackets):</p> <ul> <li>Ammonium: 1.14752.0001 (0.05-3.00 mgN/L)</li> <li>Nitrate: 1.14773.0001 (2-20 mgN/L)</li> <li>Nitrite: 1.14776.0001 (0.02-1.00 mgN/L)</li> <li>Orthophosphate: 1.14848.0001 (0.05-5.00 mgP/L)</li> </ul> <p>Regarding the <strong>spectrophotometric determination</strong>, all analyses were complemented with a blank and a standard with a known concentration of each individual nutrient component. For each nutrient, a specific wavelength was used and the resulting absorbance was converted to the associated nutrient concentration through known factors (similar to the use of calibration curves), after setting the absorbance of the blank as reference absorbance (i.e. a concentration of 0 mg/L). All of the analyses were performed with plastic 1-cm cuvettes during the first campaign, while 5-cm cuvettes were used in the remaining three campaigns due to low nutrient levels (except for nitrate, for which an analysis through 5-cm cuvettes is not supported by the used test kits).</p>
Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain)
<h2><span lang="EN-US">Dataset name</span></h2> <p><span lang="EN-US">Small_Scale_Fishery_Data_2023_v2 </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Title</span></h2> <p><span lang="EN-US">Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain). </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Description </span></h2> <p><span lang="EN-US"> This dataset was created for the Fish2Sustainability research project, which aims to evaluate how small-scale fisheries (SSF) contribute to Sustainable Development Goals (SDGs). The dataset includes 60 case studies across eight countries and was developed using a rapid appraisal framework. The framework includes a four-step process: </span></p> <p><span lang="EN-US"> 1. Identifying specific SDG targets influenced by SSF;</span></p> <p><span lang="EN-US"> 2. Extracting relevant variables from UN indicators;</span></p> <p><span lang="EN-US"> 3. Gathering expert input via a questionnaire to score these variables;</span></p> <p><span lang="EN-US"> 4. Creating composite indicators to measure SSF performance against SDGs.</span></p> <p><span lang="EN-US"> The dataset contains raw data from step 3, case study details, variable scores, and comments from data collectors (contributing authors). The dataset is valuable for researchers interested in small-scale fisheries and socio-ecological systems. By incorporating expert judgments from individuals with expertise in SSF, particularly in data-poor contexts, the dataset offers a wealth of knowledge for conducting comparative analyses across different contexts.</span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Authors </span></h2> <p><span lang="EN-US">Léopold, M.1, Bitoun, R.E.2, Beckensteiner, J.3, Chuenpagdee, R.4, Fondo, E.N.5, Akintola, S.L.6, Bach, P.7, Frangoudes, K.8, Gaibor, N.9, Gutierrez-Cala, L.10, Massey, Y.7, Randrianandrasana, R.11, Razanakoto, T.11, Saavedra-Díaz, L.M.10, Schreiber Arias, M.12,13, Salas, S.14, Devillers, R.2,4 </span></p> <h3><span lang="EN-US">Affiliations </span></h3> <p><span lang="EN-US">1 ENTROPIE (IRD, University of La Reunion, CNRS, University of New Caledonia, Ifremer), c/o IUEM, Plouzané, France </span></p> <p><span lang="EN-US">2 Espace-Dev (IRD, Univ. </span>Montpellier, Univ. Guyane, Univ. La Réunion, Univ. Antilles, Univ. Nouvelle Calédonie), Montpellier, France</p> <p>3 AMURE (Ifremer, UBO, CNRS), Plouzané, France</p> <p><span lang="EN-US">4 Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada</span></p> <p><span lang="EN-US">5 Kenya Marine and Fisheries Research Institute, Mombasa, Kenya</span></p> <p><span lang="EN-US">6 Department of Fisheries, Faculty of Science, Lagos State University, Nigeria</span></p> <p><span lang="EN-US">7 MARBEC, University of Montpellier, CNRS, Ifremer, IRD, Sète, France</span></p> <p>8 Université de Bretagne Occidentale: Brest, France</p> <p>9 Instituto Público de Investigación de Acuicultura y Pesca (IPIAP), Universidad del Pacifico (UPAC), Guayaquil, Ecuador</p> <p>10 Grupo de Investigación en Sistemas Socioecológicos para el Bienestar Humano (GISSBH), Programa de Biología, Universidad del Magdalena, Colombia</p> <p>11 Centre d’Etudes et de Recherches Economiques pour le Développement (CERED), Université d’Antananarivo, Madagascar</p> <p><span lang="EN-US">12 EqualSea Lab, Universidad Santiago de Compostela, A Coruña, Spain</span></p> <p><span lang="EN-US">13 School of Global Studies, University of Gothenburg, Gothenburg, Sweden</span></p> <p>14 Centro de Investigación y de Estudios Avanzados (CINVESTAV), IPN, Unidad Mérida, Mexico </p> <h2><span lang="EN-US">Method </span></h2> <p><span lang="EN-US">Case studies were selected in eight countries by national SSF experts, based on specific criteria and research priorities. Case studies were not selected to represent the full diversity of SSF globally or even nationally. Instead, they were chosen to capture a range of fisheries that could showcase different contributions to SDGs. SSF were defined based on various characteristics, such as resources harvested, gear used, and location of the fishery. </span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Geographical Coverage </span></h3> <p><span lang="EN-US">60 small-scale fisheries located in seven countries are documented in the data:</span></p> <ul> <li><span lang="EN-US">Colombia (4 case studies) – Pacifico: La Guajira, San Andrés y Providencia; Caribe: Chocó, Cauca, Valle del Cauca, Nariño.</span></li> <li><span lang="EN-US">Ecuador (3) – Region: Esmeraldas, Manabi, Guayas, El Oro.</span></li> <li><span lang="EN-US">France (2) – Region: Bretagne, Occitanie.</span></li> <li><span lang="EN-US">Kenya (22) – County: Kilifi, Kwale, Lamu, Mombasa, Tana River.</span></li> <li><span lang="EN-US">Madagascar (20) – Region: Analanjirofo, Anosy, Atsimo Andrefana, Boeny, Diana, Menabe, Vatovavy Fitovinany.</span></li> <li><span lang="EN-US">Mexico (2) – State: Baja California Sur, Campeche, Yucatan.</span></li> <li><span lang="EN-US">Nigeria (6) – State: Bayelsa, Cross River, Lagos, Ondo, Ogun. </span></li> <li><span lang="EN-US">Spain (1) – State: Galicia.</span> </li> </ul> <h3><span lang="EN-US">Data Collection </span></h3> <p><span lang="EN-US">Data collection took place from November 30, 2022, to July 3, 2023, spanning approximately seven months. The data presented serve as a snapshot of the conditions within a specific small-scale fishery during the assessment period. To consider the evolution of trends such as exports, economic growth, and income, we considered any relevant variables over the past decade. </span></p> <p><span lang="EN-US">Data collection approaches varied depending on the context, and data collectors received training to ensure survey consistency. We used primary data sources such as interviews, observations, and measurements whenever possible. In cases where resources were limited, we preferred secondary sources such as existing datasets and literature. Our methods were standardized, but data collectors could adjust them based on their resources. We primarily used direct observation, focus groups, and interviews to collect data. Scoring in interviews and focus groups was done directly or through group analysis by interviewers. Disagreements were resolved through additional interviews or group discussions, with secondary data used if needed. Please refer to the methods in : </span></p> <p><strong><span lang="EN-US">Bitoun et al., (2024). A methodological framework for capturing marine small-scale fisheries’ contributions to the sustainable development goals. Sustainability Science, 19(4), 1119–1137. https://doi.org/10.1007/s11625-024-01470-0. </span></strong><span lang="EN-US"><strong> </strong> </span></p> <h3><span lang="EN-US">Ethics </span></h3> <p><span lang="EN-US">Participants had the option to join of their own accord, were fully briefed on the research goals, and were given the opportunity to review interview guidelines before proceeding. Depending on the circumstances, interviews could last 45 minutes to 4.5 hours. Participants were guaranteed confidentiality and anonymity in the handling and reporting of their data.</span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Suggested citation</span></h3> <p><span lang="EN-US">Léopold, M., Bitoun, R., & Devillers, R. (2023). Qualitative Data on 61 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16077739</span></p> <h2><span lang="EN-US">Data Files</span></h2> <p><span lang="EN-US">The dataset includes the following:</span></p> <ul> <li><span lang="EN-US">The raw dataset (.xls format).</span></li> <li><span lang="EN-US">A data dictionary describing and defining each dataset column (.xls format).</span></li> </ul>
Seed Mass of species from Yasuní National Forest, Ecuador, 2000-2014
We provide data on mean dry and wet mass of > 800 species from Yasuní National Forest, Ecuador collected between 2000 and 2014. Species include trees, shrubs, lianas and herbs. We also provide data on number of seeds per fruit for >1100 species compiled in 2016, along with information on fruit type and dispersal mode. Both of these data sets supplement previously published data on flowering and fruiting phenology from this equatorial, ever-wet rainforest in eastern Ecuador (Garwood et al. 2023). Garwood, N.C., S.J. Wright, R. Valencia, and M.R. Metz. 2023. Rainforest phenology: flower, fruit and seed production from biweekly collections of 200 traps in the Yasuní Forest Dynamics Plot, Ecuador, 2000-2018 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/5e6cb3d7ff741fd9d21965c4a904bc1f (Accessed 2024-03-27).
Harmonized Soil Database of Ecuador 2021
In Ecuador there have been two main projects that have collected national soil information. These projects are: a) “Generación de Geoinformación para la Gestión de territorio y valoración de tierras rurales de la Cuenca del Río Guayas, escala 1:25.000” (2007-2015) developed by the Instituto Espacial Ecuatoriano (IEE), and b) “Generación De Geoinformación Para La Gestión Del Territorio A Nivel Nacional" (2009-2012), developed by Sistema Nacional de Información de Tierras Rurales e Infraestructura Tecnológica (SIGTIERRAS). These projects followed a similar methodology to collect and analyze soil information. However, the resulting databases have different data structures, and they show differences in the way these projects store and present soil information. Only a portion of the original databases was digitized. Most of the available data was only available in PDF files. These PDF files need to be digitized into an easy-to-manage format (e.g., *csv). The difficulty is that each PDF represents one soil profile containing morphological and analytical soil information. Thus, given the volume of soil information available in hundreds of PDF files, manual extraction (e.g., capturing soil data one by one) was not feasible. Therefore, automatic extraction of soil information from each PDF file was developed using open-source programming for data management and statistical computing (in Python and R). This process was developed to optimize data extraction from PDF files. The soil information from both projects in PDF files has been digitized and unified into one harmonized database. We present a new database for Ecuador containing soil information from 13,542 soil profiles, 5 368 are from the IEE project and 8 174 profiles from the SIGTIERRAS project. The new database includes 5368 are from the IEE project and 8174 profiles from the SIGTIERRAS project. The new database includes data from 51,692 soil horizons and information of about 20 morphological and 46 analytical variabl
Ecuador old fields permanent plot vegetation sampling
Permanent plot data is expected to show these temporal patterns: (1) rapid increases in percent cover and tree stem density, and (2) rapid turnover from early to late successional plant species. Plant-plant competition should show quick increases in intensity with native grass species and exotics as top competitors. These may lead to exclusion of some trees common after landslide disturbance. Spatial patterns of invading trees should include edge effects due to dispersal limitation with clumping of bird-dispersed species before the first five years after cow exclusion. Because of intact soil and low vegetation trees should grow, as measured by biomass(productivity), height, and basal diameter increases, significantly faster compared to colonization of landslides. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Flooded forest plot sampling in Ecuador
In order to better understand how flooding and gap formation affect Amazonian rainforests, I set up plots both in three major forest types that differed by flooding duration (referred to here as dry, wet, very wet) and in their respective gaps. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Proposal of a domain model for 3D representation of buildings for the 3D cadastre in Ecuador
<p><span>The accelerated urban sprawl of cities around the world presents major challenges for urban planning and land resource management. In this context, it is crucial to have a detailed 3D representation of buildings enriched with accurate alphanumeric information. A distinctive aspect of this proposal is its specific focus on the spatial unit corresponding to buildings. In order to propose a domain model for the 3D representation of buildings, the national standard of Ecuador and the international standard (ISO 19152) were considered. The proposal includes a detailed specification of attributes, both for the general subclass of buildings and for their infrastructure. The application of the domain model proposal was crucial in a study area located in the Riobamba canton, due to the characteristics of the buildings in that area. For this purpose, a geodatabase was created in pgAdmin4 with official information, taking into account the structure of the proposed model and linking it with geospatial data for an adequate management and 3D representation of the buildings in an open-source Geographic Information System. This application improves cadastral management in the study region and has wider implications. This model is intended to serve as a benchmark for other countries facing similar challenges in cadastral management and 3D representation of buildings, promote efficient urban development and contribute to global sustainable development.</span></p>
Cambridge butterfly wing collection - Ecuador, August 2019
<p>EN: This upload contains photographs taken by Annalie Barker and Joana Meier at the University of Cambridge, from a butterfly wing collection from Ecuador (August 2019), in collaboration with Caroline Bacquet (IKIAM). Individual sample names can be found in the information sheet. Further Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (<a href="https://heliconius.ecdb.io/">click here for the database</a>, and <a href="https://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">here for FAQ</a>). Please contact Joana Meier (jm2276[at]cam.ac.uk) or Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) for further information.</p> <p> </p> <p>ES: Este repositorio contiene fotografías tomadas por Annalie Barker y Joana Meier en la Universidad de Cambridge, de mariposas de Ecuador (Agosto 2019), en colaboración con Caroline Bacquet (IKIAM, Ecuador). Puede encontrar información sobre muestras individuales de Butterfly Genetics Group Collection en la base de datos pública Earthcape (<a href="https://heliconius.ecdb.io/">haga clic aquí para la base de datos</a>, y <a href="https://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">aquí para preguntas frecuentes</a>) Por favor, póngase en contacto con Joana Meier (jm2276 [arroba] cam.ac.uk) or Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk) con sus preguntas o peticiones.</p>
Ecuador Real State data set
<pre>This set of data contains information on different characteristics of the properties for rent on the website https://arriendayvende.com, through which analyzes can be carried out on the distribution of prices according to location, area, among others.</pre>
SAR Stack of Pichincha volcano in Ecuador, from Sentinel-1
<p>A stack of Coregistered SLCs on Pichincha volcano, Ecuador</p> <p>Sensor: Sentinel-1 Descending track 142</p> <p>Time: 2016.04.19 - 2018.12.28, 46 acquisitions</p> <p>Processor: ISCE/topsStack</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MiaplPy">MiaplPy</a>.</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "A new glassfrog of the genus Centrolene (Amphibia: Centrolenidae) from the Subandean Kutukú Cordillera, eastern Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "A new glassfrog of the genus Centrolene (Amphibia: Centrolenidae) from the Subandean Kutukú Cordillera, eastern Ecuador"</p> <p>The matrix is in NEXUS format and has 6626 bp and 239 terminals.</p> <p>Partitions are as follows:</p> <div> <div>charset 12S = 1-967;</div> <div>charset 16S = 968-2130;</div> <div> </div> <div>charset BNDFcodonPos1 = 2133-2829\3;</div> <div>charset BNDFcodonPos2 = 2131-2830\3;</div> <div>charset BNDFcodonPos3 = 2132-2828\3;</div> <div> </div> <div> </div> <div>charset ND1codonPos1 = 2832-3786\3;</div> <div>charset ND1codonPos2 = 2833-3787\3;</div> <div>charset ND1codonPos3 = 2831-3788\3;</div> <div> </div> <div> </div> <div>charset CXCR4codonPos1 = 3790-4144\3;</div> <div>charset CXCR4codonPos2 = 3791-4142\3;</div> <div>charset CXCR4codonPos3 = 3789-4143\3;</div> <div> </div> <div> </div> <div>charset cmyccodonPos1 = 4145-4547\3;</div> <div>charset cmyccodonPos2 = 4146-4548\3;</div> <div>charset cmyccodonPos3 = 4147-4549\3;</div> <div> </div> <div> </div> <div>charset POMCcodonPos1 = 4551-5160\3;</div> <div>charset POMCcodonPos2 = 4552-5161\3;</div> <div>charset POMCcodonPos3 = 4550-5162\3;</div> <div> </div> <div> </div> <div>charset RAG1codonPos1 = 5163-5616\3;</div> <div>charset RAG1codonPos2 = 5164-5617\3;</div> <div>charset RAG1codonPos3 = 5165-5618\3;</div> <div> </div> <div> </div> <div>charset SLC8A1codonPos1 = 5620-6160\3;</div> <div>charset SLC8A1codonPos2 = 5621-6158\3;</div> <div>charset SLC8A1codonPos3 = 5619-6159\3;</div> <div> </div> <div> </div> <div> </div> <div>charset SLC8A3codonPos1 = 6162-6627\3;</div> <div>charset SLC8A3codonPos2 = 6163-6625\3;</div> <div>charset SLC8A3codonPos3 = 6161-6626\3;</div> </div> <p> </p>
National Checklists 2017: Ecuador Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Ecuador collected using effechecka and geonames polygons
National Checklists 2019: Ecuador Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Ecuador collected using effechecka and geonames polygons
Rainforest phenology: flower, fruit and seed production from biweekly collections of 200 traps in the Yasuní Forest Dynamics Plot, Ecuador, 2000-2018
We provide data on flowering and fruiting phenology from an equatorial, ever-wet rainforest in eastern Ecuador, in Yasuni National Park. This is the first long-term study (18 years) of phenology in a diverse equatorial neotropical forest. Although the site is ever-wet, there is some seasonal variation in rainfall and irradiance. One major question was to determine whether the seasonal variation in climate was sufficient to drive seasonality in reproduction in this hyper-diverse forest. The study began in 2000 with various funding, and became an LTREB-funded project in 2006. We used twice monthly censuses of 200 traps to document phenology. Parts of >1000 species were identified in the traps in the 18 year period (ending early in 2018), including trees, shrubs, lianas and epiphytes. Parts identified included buds, flowers, mature fruits and mature seeds, and aborted, damaged and immature fruits and seeds. The project is on-going, and additional data will be added as it is processed.
Seedling Dynamics Demography Data, from the Yasuní Forest Dynamics Plot, Ecuador, 2002-2019
Seedling demography data are provided in annual censuses of 600 seedling plots in an equatorial, ever-wet rainforest in eastern Ecuador, in Yasuní National Park. This long-term study uses standardized methodology from the Smithsonian ForestGEO network of plots, and in particular coordination with similar studies in Luquillo, Puerto Rico, and Barro Colorado Island, Panama. We address hypotheses about the maintenance of forest diversity and long-term variation, and link our data to companion studies of flowering and fruiting phenology and sapling and adult dynamics in the Yasuní Forest Dynamics 50-ha Plot. The project is ongoing, and additional data will be added as they are processed.
canopy herbivory and leaf traits of tree communities in tropical montane rainforests of southern Ecuador
This dataset contains herbivory data estimated as leaf area loss [cm²] and [%] and several leaf traits measured either conventionally or via spectral sensing-based techniques from canopies of tree communities in tropical montane rainforests of the Andes in southern Ecuador between February and March in 2019. The data were used by Schön et al. (in prep) to evaluate whether leaf traits are valuable indicators of canopy herbivory mainly caused by arthropods and further, whether chemical leaf traits estimated via spectral sensing-based techniques have similar strong relations to herbivory as leaf traits measured conventionally. Herbivory was estimated with the software WinFOLIA ™ 2019a from scanned mature and sun-exposed leaves of tree canopies. Spectral sensing-based leaf traits comprising secondary plant metabolites and both structural and nutritional cell components were estimated from leaves with an OceanOptics spectrometer HDX. Conventionally measured leaf traits comprising morphological and nutritional traits were derived by applying both elemental and morphometrical analyses (e.g., ICP analysis, a digital micrometer and penetrometer). For detailed descriptions of the methodology see Schön et al. (in prep), Homeier et al. (2021), and Limberger et al. (2021). Research was conducted by the subprojects A1, B1, and B4 within the framework of the RESPECT project (Environmental changes in biodiversity hotspot ecosystems of South Ecuador: RESPonse and feedback effECTs) funded by the DFG with the grant numbers: BE1780/51-1, BE1780/51-2, Ho3296/6-1, FA 925/11-1, FA 925/11-2, FA 925/16-1, BR1293/17-1.
Figures 9-15 in A new species of Malthonea Thomson, 1864 (Coleoptera, Cerambycidae, Lamiinae) from Ecuador
Figures 9-15. (9) Malthoneaminima Martins & Galileo, 1995, holotype male, dorsalview (scanningof the photographsubmitted topublication – formerpersonal collectionof Ubirajara R. Martins). (10) Prymnoptetyxglaucina Thomson, 1868, holotype, dorsalview. (11) syntypesof Malthonearuficornis Belon, 1903, dorsalview (by Jesus Santiago Moure); (12) Blabia aurescens Breuning, 1966, holotype, dorsalview (by Eugenio Nearns; alsoavailable at http://cerambycids.com/longicornid/ default.asp?a=home); (13‑15) holotypeof Ptericoptusguttatus Kirsch, 1889 (by Olaf Jaeger): (13) dorsalhabitus; (14) lateralhabitus; (15) labels.
Fig. 7 in Eight new species of Ptenidiini and Discheramocephalini (Coleoptera: Ptiliidae) from Ecuador
Fig. 7. Discheramocephalus nigerrimus sp. nov. A. Habitus. B. Pronotum ×725. C. Side of head ×2220. D. Meso- and metaventra ×830. E. Mesoventrite keel and metaventrite projection between mesocoxae ×830.
ScienceDex guides
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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.