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1,753 results for “Maine”
Recruitment data from 1997 to 2021 for mussels, barnacles and rockweeds from an LTREB project in the Gulf of Maine, USA
Experimental clearings in macroalgal (Ascophyllum nodosum) stands were made in 1996 to determine if mussel beds and macroalgal stands on protected intertidal shores in New England represent alternative community states. Uncleared control plots and four sizes of circular clearings (1m, 2m, 4m and 8m in diameter), which mimicked ice scour events, were established in A. nodosum stands at 12 sites on Swan’s Island, Maine, USA. The purpose of these datasets is to provide access to data on recruitment of mussels, barnacles and fucoid seaweeds in the 60 experimental plots from 1997 to 2021. Earlier versions of the data prior to 2013 can be found in Ecological Archives (E090-039 and E096-274). This EDI version includes corrections of errors in the versions in Ecological Archives. Research was funded by NSF's LTREB program.
Northern Range Limit of Aphaenogaster picea in Maine 2015
Low temperatures at poleward range margins of terrestrial species tend to match cold tolerance limits, suggesting that range boundaries may be set by evolutionary constraints on cold physiology. The northeastern woodland ant, Aphaenogaster picea, occurs up to approximately 45 °N in central Maine. We combined presence-absence surveys with regression-tree analysis to characterize its northern range limit, and assayed two measures of cold tolerance operating on different time-scales to determine whether and how marginal populations adapt to environmental extremes. The boundary was predicted primarily by temperature, but low winter temperatures did not emerge as the primary correlate of species occurrence. Low summer temperatures and high seasonal variability predicted absence above the boundary, whereas high mean annual temperature (MAT) predicted presence in southern Maine. Locations between these zones formed an east-west band where presence was conditional on precipitation. In contrast, assays of cold tolerance across multiple sites indicated substantial local adaptation of cold tolerance at the range edge, with a 4-minute reduction in chill-coma recovery time across a 2-degree reduction in MAT. Baseline tolerance and capacity for additional plastic cold-hardening shifted in opposite directions, with hardening capacity approaching zero at the coldest sites. This trade-off suggests that populations at range edges may adapt to colder temperatures through genetic assimilation of plastic responses, potentially constraining further adaptation and range expansion.
Annual Maps of Forest Harvest Events in Maine from LANDSAT Imagery 1986-2019
We used Landsat satellite imagery and forest inventory plot measurements to develop a time series of annual maps representing potential forest harvest events for the state of Maine in the Northeastern US for the years 1986 to 2019. We first generated a set of LandTrendr temporal segmentation results for three different spectral indices. Change results were filtered to remove events greater than two years in duration, then results were combined using a seven-parameter degenerate decision trees model that determined a set of thresholds on disturbance patch size, magnitude of spectral change, and change “votes” across indices. We found that we were able to detect harvest events that removed at least 30% of total basal area with a mean F1 score of 0.72 (σ = 0.02) with a mean false negative error rate (omission) of 0.32 (σ = 0.02) and mean false positive error rate (commission) of 0.23 (σ = 0.03), and these scores further improve when maps are masked to remove human land use (built and agriculture) and water based on National Land Cover Dataset and JRC Global Surface Water classifications (mean F1 = 0.73, σ = 0.02). Comparisons with an out-of-sample reference dataset and an existing national forest disturbance dataset indicate our forest harvest maps are a locally accurate source of information for characterizing spatial and temporal variability in long-term harvest patterns across the industrial forests of northern Maine. Here, we provide annual ensemble-based maps of potential harvest events; cross-validated results, which give an indication of detection agreement across subsets of our forest inventory reference datasets; and ancillary datasets that can be used to mask false detections in urban and agricultural land uses and water.
newbi4fmri2020 Main Experiment
Open the record for dataset details and reuse information.
Sub-Alpine Lake (>600 m) High-Frequency Water Temperature, DOC (2007-2021), and Weather Station (Fall 2023) Dataset, Maine, USA.
We collected high-frequency surface and bottom water temperature in a set of nine high-elevation lakes in Maine, USA from 2007-2021. High-elevation is defined >600m above sea level. Dissolved organic carbon concentration data for the same time period and set of lakes is modified from Nelson, S.J., R.A. Hovel, J.F. Daly, A.L. Gavin, S. Dykema, and W.H. McDowell. 2021. Northeastern Mountain Ponds Geochemistry Compilation 1978-2019 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/8b51d651da0e0cff8c6ad853ef69ec3b. Air temperature and precipitation data were collected from a weather station deployed in the Mountain Pond watershed in Fall 2023 to aid comparison with low and high resolution PRISM datasets.
CO2 and CH4 fluxes from living and standing dead trees in Howland Research Forest, Maine USA, 2024
Methane (CH4) is the second-largest contributor to human-induced climate change, with significant uncertainties in its terrestrial sources and sinks. Tree stems, both living and dead, play crucial roles in forest ecosystem CH4 and carbon dioxide (CO2) flux dynamics, yet much remains unknown regarding the environmental drivers of fluxes. We measured CH4 and CO2 fluxes from 51 living trees (Picea rubens, Tsuga canadensis, Acer rubrum) along an upland-to-wetland gradient at Howland Research Forest, a net annual sink of CH4, in Maine USA. We also measured CH4 and CO2 fluxes from six standing dead red spruce stems (snags). We measured fluxes every two weeks throughout the growing season (April to November 2024) and at three heights (for a subset of red spruce stems) to capture a range of environmental conditions.
Data of predation on mussels from an LTREB project in the Gulf of Maine, USA, from 1996 to 2021.
Experimental clearings in macroalgal (Ascophyllum nodosum) stands were made in 1996 to determine if mussel beds and macroalgal stands on protected intertidal shores in New England represent alternative community states. Uncleared control plots and four sizes of circular clearings (1m, 2m, 4m and 8m in diameter), which mimicked ice scour events, were established in A. nodosum stands at 12 sites on Swan’s Island, Maine, USA. This dataset reports mussel predation on clumps of 15 mussels (Mytilus edulis) using mesh bags placed in the centers of clearings and controls. Mussel mortality was monitored in 1996, 1999, 2000, 2003, 2004, 2007 and then annually from 2010 to 2021. Causes of death were attributed to dogwhelks (Nucella lapillus), crabs (Carcinus maenas and Cancer spp.) or unknown causes. Presence and activities (e.g., drilling) of dogwhelks was also recorded. The standardized identifiers (Bay, Site, Plot) allow integration with related Environmental Data Initiative packages on community composition and recruitment of mussels, barnacles and rockweeds. Research was funded by NSF's LTREB program.
Database of fitted spectra for: Changing-Look AGNs - I. Tracking the transition on the main sequence of quasars
<h3>Results from the spectral fitting for a sample of changing-look active galactic nuclei (AGNs) with SDSS spectroscopy using PyQSOFit.</h3>
Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023)
<h3>Background</h3> <p>Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities (e.g. fruits, nuts, and meat) significantly impacts the Global Commons, encompassing the climate system, ice sheets, land biosphere, oceans, and the ozone layer. The convertion of natural forests into areas dedicated to these activities lead to disrupted ecosystems (Foley et al. 2005), severely degraded biodiversity (Newbold et al. 2015), and the release of substantial amounts of greenhouse gases (GHGs) into the atmosphere (Hong et al. 2021), further exacerbating climate change and ocean acidification (Doney et al. 2009). The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally, with other industries like timber and mining also playing significant roles (Curtis et al. 2018). To achieve global climate targets, forestry, and other land use GHG emissions must decrease along a nonlinear trajectory and reach carbon neutrality by 2050 (Rockström et al. 2017). However, to successfully address this road map, improving our understanding of deforestation drivers is urgently needed.</p> <h3>Summary</h3> <p>This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.</p> <p>In order to detect spatially-explicit deforestation drivers, the current extent of commodities and agricultural products was overlapped with global annual tree cover loss in the 10-year period from 2014 to 2023. Carbon stocks in the deforested areas were then assumed to have been emmited into the atmosphere. Recent, detailed crop and pasture maps for relevant commodities were used whenever available, and coarser resolution datasets were used as supplements when needed. Operations were performed in Google Earth Engine.</p> <h3>Datasets used</h3> <p><em>Forest and biomass carbon distribution</em></p> <p>The <a href="https://earthenginepartners.appspot.com/science-2013-global-forest">Global Forest Change</a> dataset (Hansen et al., 2013) is used to estimate deforestation between 2014 and 2023. This tree cover loss dataset measures the first instance of complete removal of tree cover canopy at a 30-meter resolution for all woody vegetation over 5 meters in height.</p> <p>The <a href="https://data-gis.unep-wcmc.org/portal/home/item.html?id=374a99fc76574f72bb8c71af7b428d0a">WCMC Above and Below Ground Biomass Carbon Density </a>(Soto-Navarro et al., 2020), for reference year 2010 at 300m pixel, is overlapped with resulting deforested areas pixels to dermine the biomass carbon present in the areas before deforestation.</p> <p><em>Generalized deforestation drivers</em></p> <p><a href="https://data.globalforestwatch.org/documents/ff304784a9f04ac4a45a40f60bae5b26/about">Tree cover loss by dominant driver</a> (Curtis et al., 2022) in 2023 is used to determine wide categories of deforestation drivers (commodities, shifting agriculture, forestry, wildfire and urbanization). Pixels indicating deforestation in the Global Forest Change dataset (Hansen et al., 2013) that overlap the commodities and shifting agriculture pixels from this dataset (Curtis et al., 2022) have their drivers further detailed with the data sources listed in the below.</p> <p><a href="http://www.earthstat.org/">EarthStat</a> pasture areas layer (Ramankutty et al., 2008) is used to identify areas for which specific livestock categories are to be defined. The project provides pasture areas for reference year 2000 at ~10km resolution.</p> <p><em>Detailed deforestation drivers</em></p> <p>The <a href="https://earthobservations.org/geoglam.php">Group on Earth Observations Global Agricultural Monitoring</a> (GEOGLAM) commodity distibution layer (Becker-Reshef et al., 2023) is used to identify specific commodities (winter wheat, spring wheat, maize, rice and soybean) to deforestation pixels pertaining to the "commodities" class. The ressource provides commodity distribution mapping at 5km pixel resolution. Values are provided as percentage of pixel area occupied by given crop.</p> <p>The <a href="https://mapspam.info/">Spatial Production Allocation Model (SPAM)</a> physical area layer (You et al., 2014) for reference year 2020 is used to detail drivers pertaining to the "shifting agriculture" class. The dataset covers 46 crops and crop groups at ~9km pixel resolution. Values are provided as percentage of pixel area occupied by given crop or crop group.</p> <p>The <a href="https://www.fao.org/livestock-systems/global-distributions/en/">Gridded Livestock of the World (GLW3)</a> (Gilbert et al., 2022) is used to determine which species (cattle, goat, sheep or horse) of livestock is raised in areas identified as pasture in the EarthStat layer and pertaining to the "commodities" class. The project provides livestock distribution for reference year 2015 at ~9km resolution. Values are provided as number of individuals located within the pixel. Values were converted into percentage of pixel area covered by grazing field for given species based on species density thresholds.</p> <h3>Data processing</h3> <p>Most of data processing takes place in Google Earth Engine, with scripts redacted in javascript. In summary, two strategies were implemented:</p> <p><strong>Proportional driver distribution strategy</strong>: When deforestation pixels (Hansen et al., 2013) overlapped with pixels from at least one of the detailed deforestation drivers data sources, the driver describe in the latter were associated with that deforested area. Whenever more than one of these data sources had non-null pixels overlapping the area, a proportional distribution was assumed (i.e. if SPAM indicated 100% of the area to be covered by cowpea crops, GEOGLAM 100% by maize, and GLW3 100% by cattle grazing fields, the pixel is assumed to have 33.3% of its deforested area associated with each of these drivers).</p> <p><strong>Main driver strategy</strong>: When deforestation pixels did not overlap with any non-null pixels from any of the detailed drivers sources, the pixel is assumed to have the entirety of its deforested area associated with one single main driver resulting from a crop-livestock mosaic. The mosaic is created by taking the highest value from each of the crop or livestock distribution rasters, and then assigning the raster category to be the new pixel value, ultimately creating a category raster layer containing the main crop, crop group or livestock species occupying that pixel area. Null or zero values in this mosaic are filled-in by nearest neighbour analysis, to a limit of 20 pixels expansion. This was enough to ensure that all deforestation pixels had at least one detailed driver with which it could be associated. The logic behind this operation resides in the fact that the deforestation layer (Hansen et al., 2013) has a larger temporal coverage (with the more recent data point being the reference year 2023), while the detailed driver layers can be as old as reference year 2015. This means we're assuming the main deforestation drivers continued to expand their limits to neighbouring areas during the years for which no data is available.</p> <p>Resulting rasters from both strategies are put together and a zonal statistics operation is performed in order to populate the vector grid cells.</p> <h3><strong>Files</strong></h3> <p>This repository contains the following files:</p> <ul> <li><em>deforested_area_by_LUC_driver_2014_2023</em>.CSV contains the deforested area (hectares) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>carbon_emissions_by_LUC_driver_2014_2023</em>.CSV contains the carbon emitted (Mg CO2 eq.) and the corresponding driver in each grid cell (idenfied by the id field) in each year, in CSV text format.</li> <li><em>spatial_grid</em>.gpkg contains the raw 50km cell grid, with identification of country (iso3 and name fields), region, and FAO agroecological zone (zone field) and thermal zone (thermal field), in Geopackage format. In order to visualize the data in a map, the user will need to join one of the csv files to this geopackage file by basing the join on the 'id' field.</li> <li><em>summary_showcase</em>.png is an image showcasing maps created using the database, as well as a diagram showing the datasets used to create the final dataset.</li> </ul> <h3><strong>How to cite</strong></h3> <p>Iablonovski, G.; Berthet, E. C.; Roberts, S. (2024). Yearly CO2 emissions from anthropogenic land use change by main driver (2014-2023) [Data set]. Zenodo. https://zenodo.org/doi/10.5281/zenodo.13308514</p> <h3>Authors and contact</h3> <p>Authors: Guilherme Iablonovski*, Etienne Charles Berthet, Sophie Roberts</p> <p>*Corresponding author: Guilherme Iablonovski (guilherme.iablonovski@unsdsn.org)</p>
BioReCer Main biological feedstock flows database
<p>BioReCer aims at assessing and complementing current certification schemes for biological resources according to the new EU sustainability goals to enhance bio-based circular systems.</p> <p>This will be achieved by including new criteria that align with EU taxonomy and EU corporate due diligence regulations into guidelines for certifying biological resources’ sustainability, origin, tracking and traceability (T&T), and by ensuring applicability at EU and global scale.</p> <p>By promoting the sustainability and trade of biological resources, BioReCer will increase the added value, use, as well as social acceptance of bio-based products.</p> <p>Part of the specific objectives of the project is to map the current European biomass flows in 4 main sectors:</p> <ol> <li>Fishery</li> <li>Urban waste and wastewater</li> <li>Agriculture</li> <li>Forestry</li> </ol> <p>This database presents information on over 30 biomass feedstocks from across this sectors and is estimated to cover approx. 90% of the available secondary biological feedstocks in the EU. </p> <p>Table 1 shows the amount of secondary biomass produced by feedstock and their fates.</p> <p>Table 2 shows the amount of primary biomass prodused, imported and exported to and from Europe.</p> <p>This database is based on the work done for derivable 2.1 - Main biological feedstocks flows.</p>
Shapefiles of administrative boundaries, Subway and main rivers in Glasgow, UK, around 1910
<p>This collection consists of ESRI shapefiles for Glasgow around 1910:</p> <ul> <li>sanitary district boundaries in 1903 (Sanitary_Districts.shp, etc.)</li> <li>municipal ward boundaries in 1912 (Wards_1912.shp, etc.)</li> <li>registration district boundaries within the area of the City of Glasgow in 1913 (Registration_Districts.shp, etc.)</li> <li>routes of main rivers (River Clyde and River Kelvin) around 1915 (Rivers.shp, etc.)</li> <li>route of the Glasgow Subway around 1915 (Subway.shp, etc.)</li> </ul> <p>For details of shapefile construction, please see the descriptions in the following article:</p> <p>Angelopoulos, K., Stewart, G. and Mancy, R. <em>Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986.</em></p> <p>Details of construction and references to original map sources are provided in the second paragraph of the section "Geographic conversion" in the online supplementary materials of the above reference. Further information about the boundaries is provided in the caption of Figure S1 of the supplementary materials. Additional contextual information is provided in both the main text and supplementary materials.</p>
Intertidal Crab Data in Midcoast Maine: 2018-2024
Invasive species have caused major disruptions to ecosystems worldwide. The European green crab invaded North America in the 1800s and is considered one of the world’s 100 worst invaders by the IUCN. Observations of spatiotemporal green crab population dynamics are essential for predicting and managing the ecological and economic impacts of this harmful invasive species. These data come from a standardized method for assessing green crab population dynamics in the rocky intertidal zone of New England and Atlantic Canada. Data was collected from 10 survey sites located along the coast of Maine from Yarmouth, ME (furthest point south), to Walpole, ME (furthest site north) from January, 2018 to November, 2024. Data was collected following the survey design described in McMahan (2020), using a 1m^2 quadrat to sample rocky intertidal habitat. The resulting data collected using this protocol has a wide range of uses, including to inform ecological research, conservation efforts, mitigation strategies, and fishery development, as well as for educational outreach purposes.
Densities and cover data for intertidal organisms from an LTREB project in the Gulf of Maine, USA, from 1996 to 2023.
Experimental clearings in macroalgal (Ascophyllum nodosum) stands were made in 1996 to determine if mussel beds and macroalgal stands on protected intertidal shores in New England represent alternative community states. Uncleared control plots and four sizes of circular clearings (1m, 2m, 4m and 8m in diameter), which mimicked ice scour events, were established in A. nodosum stands at 12 sites on Swan’s Island, Maine, USA. The purpose of these datasets is to provide access to data on densities and percentage cover in the 60 experimental plots from 1996 to 2023. Earlier versions of the data prior to 2007 can be found in Ecological Archives ( E087-047 and E089-032). The current EDI version includes corrections of errors in the versions in Ecological Archives. Data include densities of mussels (Mytilus edulis), an herbivorous limpet (Testudinalia testudinalis), herbivorous snails (Littorina littorea, Littorina obtusata), a predatory snail (Nucella lapillus), a barnacle (Semibalanus balanoides), and fucoid algae (Ascophyllum nodosum and Fucus vesiculosus), and percentage cover by mussels, barnacles, fucoids and other sessile organisms. Research was funding by NSF's LTREB program.
Larval transport pathways from three prominent sand lance habitats in the Gulf of Maine: otolith data, model data, and post-processed model data products
This dataset includes hatch and larval period for sand lance collected in 2019 and results from particle tracking runs of simulated sand lance larvae throughout the Northeast U.S. Shelf as part of Long-Term Ecological Research (NES-LTER). Release dates vary by region, corresponding to hatch and settlement dates of settling sand lance collected in 2019. Particles were depth-keeping throughout the upper 40 m to best replicate our understanding of the vertical distribution of sand lance larvae. Data were used to determine the average particle transport pathways from these sand lance habitats, including connectivity among the three hotspots, and spatial variability of connectivity within each hotspot. Further information can be found within the manuscript: Suca, J. J., Ji, R., Baumann, H., Pham, K., Silva, T. L., Wiley, D. N., Feng, Z., & Llopiz, J. K. (2022). Larval transport pathways from three prominent sand lance habitats in the Gulf of Maine. Fisheries Oceanography, 31( 3), 333-352. https://doi.org/10.1111/fog.12580
Modern China Geospatial Database - Main Dataset
<p>MCGD_Data_V2.2 contains all the data that we have collected on locations in modern China, plus a number of locations outside of China that we encounter frequently in historical sources on China. All further updates will appear under the name "MCGD_Data" with a time stamp (e.g., MCGD_Data2023-06-21)</p> <p>You can also have access to this dataset and all the datasets that the ENP-China makes available on GitLab: https://gitlab.com/enpchina/IndexesEnp</p> <p>Altogether there are 464,970 entries. The data include seven variables:<br>- Name: Place names and their variants in Chinese, pinyin, and any recorded transliteration<br>- Prov_Zh: Chinese province names in Chinese characters (新疆, 江蘇, 河北, etc.)<br>- Prov_Py: Chinese province names in pinyin<br>- LAT: Latitude coordinates<br>- LONG: Longitude coordinates<br>- LocID: Location identifiers<br>- NameID: Location name identifiers</p> <p>The Name IDs all start with H followed by seven digits. This is the internal ID system of MCGD.</p> <p>Locations IDs that start with "D" are data points extracted from China Historical GIS (Harvard University); those that start with "E" are locations extracted from the data points in Geonames or data points we have added from various map sources.</p> <p>One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates.</p> <p>From June 2021 onward, we have adopted a different file naming system to keep track of versions. From MCGD_Data_V1 we have moved to MCGD_Data_V2. In June 2022, we introduced time stamps, which result in the following naming convention: MCGD_Data_YYYY.MM.DD. </p> <p> </p> <p><strong>UPDATES</strong></p> <p><strong>MCGD_Data2025_08_06</strong> introduces a significant update with the addition of the <strong>‘Code’</strong> column. This column categorizes place names as follows:</p> <ul> <li> <p><strong>A</strong>: Canonical Chinese name</p> </li> <li> <p><strong>C</strong>: Alternative Chinese name</p> </li> <li> <p><strong>P</strong>: Romanized name in pinyin</p> </li> <li> <p><strong>W</strong>: Romanized name in another transliteration system</p> </li> </ul> <p>When the codes <strong>P</strong> or <strong>W</strong> are doubled (<strong>PP</strong>, <strong>WW</strong>), this indicates that the place name does not match any existing Chinese name in the dataset. These unmatched names will be reviewed and linked progressively, rather than through a systematic batch process, due to their high volume.The coding system is designed to facilitate name-matching operations between MCGD and place names extracted from historical sources using programming tools. It also enables filtering for more precise and efficient matching. The dataset contains a total of <strong>472,749 entries</strong>.</p> <p>MCGD_Data2025_02_28 includes a major change with the duplication of all the locations listed under Beijing, Shanghai, Tianjin, and Chongqing (北京, 上海, 天津, 重慶) and their listing under the name of the provinces to which they belonge origially before the creation of the four special municipalities after 1949. This is meant to facilitate the matching of data from historical sources. Each location has a unique NameID. Altogether there are 472,818 entries</p> <p>MCGD_Data2025_02_27 inclues an update on locations extracted from Minguo zhengfu ge yuanhui keyuan yishang zhiyuanlu 國民政府各院部會科員以上職員錄 (Directory of staff members and above in the ministries and committees of the National Government). Nanjing: Guomin zhengfu wenguanchu yinzhuju 國民政府文官處印鑄局國民政府文官處印鑄局, 1944). We also made corrections in the Prov_Py and Prov_Zh columns as there were some misalignments between the pinyin name and the name in Chines characters. The file now includes 465,128 entries.</p> <p>MCGD_Data2024_03_23 includes an update on locations in Taiwan from the Asia Directories. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown").</p> <p>MCGD_Data2023.12.22 contains all the data that we have collected on locations in China, whatever the period. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). The dataset also includes locations outside of China for the purpose of matching such locations to the place names extracted from historical sources. For example, one may need to locate individuals born outside of China. Rather than maintaining two separate files, we made the decision to incorporate all the place names found in historical sources in the gazetteer. Such place names can easily be removed by selecting all the entries where the 'Province' data is missing.</p>
Upward, MeV-class electron beams over Jupiter's Main Aurora; Selected data for
<p>This submission provides selected ASCII data that is utilzed in a scientific study entitled: "Upward, MeV-class electron beams over Jupiter’s Main Aurora". A PDF of the manuscript is included here. The 13 authors of this study are identified in the PDF manuscirpt. The data files are labeled according to the figure numbers and panels used in the manuscript. The PDF of the paper serves to document the qualities of the data submitted. The abstract of the manuscript is as follows: </p> <p>Abstract: Jupiter’s poleward (Zone II) main aurora exhibits bi-directional electron acceleration; upward acceleration dominates but downward acceleration generates strong aurora. During Juno’s first perijove (PJ1), the upward acceleration manifested as narrow electron angular beams (within ~5 of the magnetic field) over the 30-1200 keV energy range of Juno’s Jupiter Energetic Particle Detector Investigation (JEDI). These beams can be simply connected (non-uniquely) to >10 to perhaps 100’s of MeV electrons that penetrated the radiation shielding of the camera head of the Magnetometer Investigation’s Advanced Stellar Compass (ASC). The most intense of those multiple MeV populations are shown to have been highly directional and propagating upwards. How auroral processes generate such beams is unknown. With azimuthal symmetry assumed (not demonstrated here), these beams provided >1026 s-1 of >30 keV electrons to Jupiter’s vast magnetosphere, a possibly critical and dominating source of energetic electrons to that region and ultimately to Jupiter’s radiation belts.</p>
Limnological data from Jalisco (Mexico) main eastern basins
<p>This collection includes limnological data from the main basins of eastern Jalisco (Mexico), in addition to phytoplankton data from Lake Cajititlán. The biological data belongs to the Institute of Limnology of the University of Guadalajara, while the water quality data were retrieved from the State Water Comission of Jalisco's public website.</p> <p>The following basins are considered:</p> <ul> <li>Lake Cajititlán (2009 - 2024)</li> <li>Lake Zapotlán (2009 - 2004)</li> <li>Río Verde (2015 a 2024)</li> <li>Río Zula-Lerma (2020 a 2024)</li> <li>Río Santiago (2009 a 2024)</li> </ul> <p>The dataset contains, for most basins, the following variables:</p> <ul> <li>pH, temperature, transparency, turbidity, deepness.</li> <li>Dissolved oxygen, conductivity, COD, BOD, alkalinity, suspended and dissolved solids.</li> <li>Chlorines, fluorides, phosphorus, nitrides, nitrites, sulfides, sulfates, phenols.</li> <li>Metals (Al, As, Ba, Cd, Cu, Cr, Mn, Mg, Ni, Pb, Na, Zn, Ag).</li> <li>Fecal coliforms; A, B and C Chlorophylls</li> <li>Phytoplankton counts for Lake Cajititlán (2014-2019)</li> </ul> <p>Additional information on the variables and sample sites can be consulted in the file "unidades.csv" and "sitios.csv" respectively</p>
QBO: monthly zonal stratospheric winds from tropical radiosonde data (mainly Singapore)
<p><strong>Monthly Tropical Stratospheric Zonal Winds from Radiosondes</strong></p> <p><strong>Data Source and Processing:</strong></p> <p>Monthly mean zonal wind data for the tropics are provided as a service for the global QBO and trend analysis communities. The original data source and processing chain were established by the Free University of Berlin (FUB). Currently, the data is processed at the Karlsruhe Institute of Technology (KIT, ROR:04t3en479), Institute of Meteorology and Climate Research (IMK), Germany with the tools developed at FUB.</p> <p><strong>Data Description:</strong></p> <p>The dataset includes monthly mean zonal wind values at pressure levels 100, 90, 80, 70, 60, 50, 45, 40, 35, 30, 25, 20, 15, 12, and 10 hPa, derived from radiosonde observations at four equatorial stations:</p> <ul> <li>Kiribati (Canton Island) - data from 1953 to 1967 (closed)</li> <li>Maldives (Gan Island) - data from 1967 to 1975 (closed)</li> <li>Singapore (Payalebar) - data from 1975 to 1989</li> <li>Singapore (Changi) - data from 1989 onwards</li> </ul> <p><strong>Important Notes:</strong></p> <ul> <li>Values for 100 hPa from October 1967 are solely from Singapore (Changi).</li> <li>Values for 100 hPa before October 1967 are from Kiribati (Canton Island) when available.</li> <li>Detailed information about the radiosonde stations and their periods of operation is provided below.</li> </ul> <p><strong>Additional Information:</strong></p> <ul> <li>Access the data in other formats also published here: <a href="https://www.atmohub.kit.edu/english/807.php" target="_blank" rel="noopener noreferrer">https://www.atmohub.kit.edu/english/807.php</a></li> </ul> <p><strong>Detailed List of Radiosonde Stations:</strong></p> <div> <div> <div> <div> <table> <tbody> <tr> <th>Station Name</th> <th>Location (Lat, Lon)</th> <th>Data Period</th> <th>Pressure Levels (hPa)</th> </tr> <tr> <td>Kiribati (Canton Island)</td> <td>-2.7667, -171.7167</td> <td>1953 - 1967 (closed)</td> <td> <p>Above 100 (until August 1967)</p> <p>100 (until September 1967)</p> </td> </tr> <tr> <td>Maldives (Gan Island)</td> <td>-0.6933, 73.1556</td> <td>1967 - 1975 (closed)</td> <td>Above 100 (September 1967 to December 1975)</td> </tr> <tr> <td>Singapore (Payalebar)</td> <td>1.3667, 103.9167</td> <td>1975 - 1989</td> <td>Above 100 (January 1976 to May 1989)</td> </tr> <tr> <td>Singapore Upper Air Observatory</td> <td>1.3404, 103.8879</td> <td>1989 - present</td> <td> <p>100 (October 1967 to May 1989), </p> <p>All levels from June 1989</p> </td> </tr> </tbody> </table> </div> </div> </div> </div> <div> </div>
Prevalent trends in realized probability of occurrence of main European forest tree species for 2000–2020
<p>High resolution maps resulting from a trend analysis conducted for the period 2000–2020 on the probability of occurrence maps prepared by <a href="https://doi.org/10.7717/peerj.13728">Bonannella et al. (2022)</a>. For this analysis we selected the realized distribution time series layers at 30m spatial resolution for 6 out of 16 species described in the mentioned publication:</p> <ul> <li>Silver fir (<em>Abies alba </em>Mill.)</li> <li>European beech (<em>Fagus sylvatica </em>L.)</li> <li>Norway spruce (<em>Picea abies </em>L.)</li> <li>Black pine (<em>Pinus nigra </em>J. F. Arnold)</li> <li>Scots pine (<em>Pinus sylvestris </em>L.)</li> <li>Common oak (<em>Quercus robur </em>L.)</li> </ul> <p>The trend analysis was conducted per pixel on each of these species individually. We fitted simple OLS regression models with the probability of occurrence as the dependent variable and time as the independent variable. After the model fitting, we also calculated the t-test statistics to determine the presence of an increasing (positive) or decreasing (negative) trend or no trend at all.</p> <p>By combining the regression slope coefficient (<em>β</em>) and the <em>p</em>-value from the t-test statistics we assigned each pixel to one of three classes:</p> <ul> <li><em>positive</em>: <em>β</em> > 0.25 AND <em>p</em>-value < 0.05</li> <li><em>negative</em>: <em>β</em> < −0.25 AND <em>p</em>-value < 0.05</li> <li><em>no trend / stable</em>: −0.25 ≤ <em>β</em> ≥ 0.25 OR <em>p</em>-value > 0.05</li> </ul> <p>We then aggregated the resulting classes at 1km resolution maps to capture the prevalent trend in probability of occurrence over a certain area. Files are named according to the following naming convention, e.g.:</p> <ul> <li>veg_abies.alba_slope_30m_0..0cm_epsg3035_v1.0</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>abies.alba</strong>,</li> <li>variable name: e.g. <strong>slope</strong>,</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v1.0</strong>.</li> </ul> <p>For each species here we provide the following layers:</p> <ul> <li>veg_abies.alba_<strong>slope</strong>:<strong> </strong>slope coefficient (scaling factor: 10000)</li> <li>veg_abies.alba_<strong>pvalue</strong>:<strong> </strong><em>p</em>-value (scaling factor: 1000)</li> <li>veg_abies.alba_<strong>pos.trends_30m</strong>: pixels classified as <em>positive </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>pos.trends_1km</strong>: proportion of pixels of the <em>positive </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>neg.trends_30m</strong>: pixels classified as <em>negative </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>neg.trends_1km</strong>: proportion of pixels of the <em>negative </em>class over a 1×1 km area (range 0–100)</li> <li>veg_abies.alba_<strong>no.trends_30m</strong>: (pixels classified as <em>no trend / stable </em>on the original maps at 30m resolution (boolean layer with range 0–100, only the two extremes values are present)</li> <li>veg_abies.alba_<strong>no.trends_1km</strong>:<strong> </strong>proportion of pixels of the <em>no trend / stable </em>class over a 1×1 km area (range 0–100)</li> </ul> <p>Files are provided as GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in <em>QML</em> format</p> <p>A publication describing, in detail, all processing steps is currently in review. See at:<br> <br> Bonannella, C., Parente, L., de Bruin, S. and Herold, M. (2023). Multi-decadal trend analysis and forest disturbance assessment of European tree species: concerning signs of a subtle shift, PREPRINT (Version 1) available at Research Square [<a href="https://doi.org/10.21203/rs.3.rs-3288937/v1">https://doi.org/10.21203/rs.3.rs-3288937/v1</a>]</p> <p> </p>
Ice Phenology for 58 Lakes in Maine, USA, 2002/2003-2017/2018
This dataset contains ice phenology for 58 lakes in Maine, USA between winter 2002/2003 and 2017/2018 from the Lake Stewards of Maine Volunteer Lake Monitoring Program, Maine Department of Environmental Protection, and the Auburn Water District/Lewiston Water Division. Ice-off data in this dataset are available for all 58 lakes, ice-on data are available for 13 lakes. These data are a subset of all ice phenology data available from each of the data sources. Lakes had at least four years of ice phenology data and each lake is at least 3 km2 to facilitate the pairing of MODIS temperature data.
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