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1,069 results for “Data managment”
Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape
Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are comprised of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked 1) what is the relative importance of different variables driving fire severity, and 2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 SE) versus BLM forests (398.87 ± 18.23 SE) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</p>
Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat
<p>Highly mobile species, such as migratory birds, respond to seasonal and inter-annual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally-resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16-days), and 17-year long-term average, surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. While modeling with both real-time and long-term average data provided good fit to withheld validation data (0.79 < AUC < 0.89 across taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g. perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration. --</p>
Greater Prairie-chicken data used in "Responses to land cover and grassland management vary across life-history stages for a grassland specialist"
<p>Grassland birds have exhibited dramatic and widespread declines since the mid-20th century. Greater Prairie-Chickens (<i>Tympanuchus cupido pinnatus</i>) are considered an umbrella species for grassland conservation and are frequent targets of management, but their responses to land use and management can be quite variable. We used data collected during 2007-2009 and 2014-2015 to investigate effects of land use and grassland management practices on habitat selection and survival rates of Greater Prairie-Chickens in central Wisconsin, USA. We examined habitat, nest-site, and brood-rearing site selection by hens and modeled effects of land cover and management on survival rates of hens, nests, and broods. Prairie-chickens consistently selected grassland over other cover types, but selection or avoidance of management practices varied among life-history stages. Hen, nest, and brood survival rates were influenced by different land cover types and management practices. At the landscape scale, hens selected areas where brush and trees had been removed during the previous year, which increased hen survival. Hens selected nest sites in hay fields and brood-rearing sites in burned areas, but prescribed fire had a negative influence on hen survival. Brood survival rates were positively associated with grazing and were highest when home ranges contained ≈15-20% shrub/tree cover. The effects of landscape composition on nest survival were ambiguous. Collectively, our results highlight the importance of evaluating responses to management efforts across a range of life history stages, and suggest that a variety of management practices are likely necessary to provide structurally heterogeneous, high-quality habitat for Greater Prairie-Chickens. Brush and tree removal, grazing, hay cultivation, and prescribed fire may be especially beneficial for prairie-chickens in central Wisconsin, but trade-offs among life-history stages and the timing of management practices must be considered carefully.</p>
Data set of 1) plant abundance in the herb layer and 2) Shrub and tree composition and structure following forest management along a chronosequence
<p>In each site (1200 m²), three circular plots (400 m²) were established (total of 198 plots). Data presented here for 1) plant of the herb layer and 2) shrub and tree are grouped by site (addition of three plots). </p> <p>In the herb layer, total plant species identity and abundance (percentage cover) were sampled in each plot using eight circular micro-plots of 4 m². Data were collected in 2016 and 2017, between June and August. To minimize seasonal variability and allow detection of early spring species, plants (identity and abundance) in the herb layer were measured twice (once in June to early-July, and once in late-July to August).</p> <p>For tree, in each plot, species identity, diameter at breast height (DBH, 1.3 m) and locations of each tree > 9.1 cm DBH were determined. In each plot, species identity and DBH of shrubs and small trees (DBH range: 1.1 to 9.1 cm) were measured in three circular micro-plots of 25 m². Data that were related to the shrub-canopy layer included all trees and shrubs with DBH > 1.1 cm. Here, in each site, forest composition and structure is represented by different combinations of DBH classes (1.1-4 cm, 4-9.1 cm, 9.1-20 cm, 20-35 cm, >35 cm) and species.</p> <p>Plant community composition and abundance were assessed in unmanaged forests (sites of old-growth forest > 100 years, with dominant and co-dominant trees older than 200 years, and no obvious sign of past harvesting), and in even-aged and uneven-aged managed forests along a chronosequence (< 5 years, 15 years, 30 years after forest harvesting).</p>
WORLDCOM Data Management Plan
<p>Data Management Plan for the OHEJP WorldCOM project.</p>
Data from: Bee diversity decreases rapidly with time since harvest in intensively managed conifer forests
<p>Despite widespread concern about the anthropogenic drivers of global pollinator declines, little information is available about the impacts of land management practices on wild bees outside of agricultural systems, including forests managed intensively for wood production. We assessed changes in wild bee communities with time since harvest in 60 intensively managed Douglas-fir (<em>Pseudotsuga</em> <em>menziesii</em>) stands across a gradient in stand ages spanning a typical harvest rotation. We measured bee abundance, species richness, and alpha and beta diversity, as well as habitat characteristics (i.e., floral resources, nesting substrates, understory vegetation, and early seral forest in the surrounding landscape) during the spring and summer in 2018 and 2019. We found that bee abundance and richness declined rapidly with stand age, decreasing by 61% and 48%, respectively, for every five years since timber harvest. Asymptotic estimates of Shannon and Simpson diversity were highest in stands 6-10 y post-harvest and lowest after the forest canopy had closed, approximately 11 y post-harvest. Bee communities in older stands were nested subsets of bee communities found in younger stands, indicating changes were due to species loss, rather than turnover, as stands aged. Bee abundance – but not richness – was positively associated with floral resource density, and neither metric was associated with floral richness. The amount of early seral forest in the surrounding landscape seemed to enhance bee species richness in older, closed-canopy stands, but otherwise had little effect. Changes in the relative abundance of bee species did not relate to bee functional characteristics such as sociality, diet breadth, or nesting substrate. Our study demonstrates that Douglas-fir plantations develop diverse communities of wild bees shortly after harvest, but those communities erode rapidly over time as forest canopies close. Therefore, stand-scale management activities that prolong the pre-canopy closure period and enhance floral density during the initial stage of stand regeneration will provide the greatest opportunity to enhance bee diversity in landscapes dominated by intensively managed conifer forests.</p>
Data from : Quantifying the Effect Size of Management Actions on Aboveground Carbon Stocks in Forest Plantations
<p>These data were extracted from publications listed in the supplemental data associated with the manuscript. The data include information on the aboveground carbon biomass of individual tree crops in pure versus interplanted stands, the aboveground biomass stocks of NPK fertilized versus unfertilized forest plantations, and the aboveground carbon stocks of thinned versus unthinned forest plantations. Additionally, the data contains other attributes of the studies that were used as moderator variables in the meta-analysis. For further details on how missing standard deviations were imputed, please refer to the methods section of the manuscript.</p>
Data from: Publication and use of genetics tools in conservation management applications: a systematic review
<p><span>Genetic tools are used in applied conservation management for taxonomic identification, delineation of management units, management of wild populations, captive breeding and reintroduction, and control of invasive species, disease, and hybridisation. </span></p> <p><span>To assess the extent to which genetics tools are being used for applied conservation management, we conducted a systematic literature review of over 53,767 papers focussing on wildlife research that reported results on species delineation, translocations, and population augmentation. We synthesised information on papers that used genetics tools in an expressly applied manner across all wildlife species. </span></p> <p><span>We found that the application of genetics tools in conservation management was biased towards fishes, mammals, and birds and northern hemisphere locations, especially the USA and Europe. </span></p> <p><span>Despite genetics tools being a highly published topic, it was difficult to find published applications of these tools in both the primary and the grey literature. Of the 115 papers on 152 species that could be considered an applied use of a genetics tool expressly for conservation management, only 49 had definable applied outcomes. The remaining 66 made recommendations, but it was often unclear if the recommendations were ever used to make conservation management decisions because of the time-lag between publication of the initial recommendation and publication of the results of the use of the tool in a conservation management situation, as well as the lack of dissemination in the primary literature. </span></p> <p><span>Our study highlights the relatively low publication rate of applications of genetics tools compared to the general conservation genetics field. These tools appear to have either a low percentage of translations into publication ('conservation genetics publishing gap') or a poor uptake among wildlife conservation managers ('conservation genetics gap')—the two are indistinguishable in this review. </span></p> <p><span><em>Policy implications</em>. Conservation genetics tools must be brought to the forefront of conservation policy and management. Users should support the use of systems and accessible databases to increase the uptake of genetic tools for conservation in applied management decisions for wildlife, reducing barriers to disseminating the results to other end users and interested parties.</span></p>
Test Dataset for MapSeq Data Manager Galaxy Wrapper
<p>Test Dataset used to test the Data Manager for the MapSeq Galaxy Wrapper</p>
Ecosystem management policy implications based on Tonga main tuna species catch data 2002–2018
<p>From 2002 to 2018, the Tongan Long Line fishery collected and compiled catch data (limited to presence records) for Albacore, Bigeye, Skipjack, and Yellowfin. The Tonga Ministry of Fishery and the South Pacific Community (SPC) Office in New Caledonia provided this data. To ensure adherence to regulations, at least two fisheries offices cross-verified the entire fish catch. The data consists of daily fishing positions (latitude and longitude) and date (day, month, and year), organized in a 1^0 spatial grid. Our study utilized catch per unit effort (CPUE), a standardized metric indicating fishing efficiency and effort, calculated by dividing the weight of the catch in metric tons by the number of hooks deployed per fishing record. To align with the temporal scales of the predictor variables, we aggregated the CPUE data into monthly and annual datasets using Microsoft Excel.</p>
Heart Failure Patient Management and Interventions Using Continuous Patient Monitoring Outside Hospitals and Real-world Data
ClinicalTrials.gov study NCT07008729. IPD Sharing: YES. Countries: 4. Publications: 12.
This Study Will Collect Clinical and Patient Reported Experience Data From Participants Requiring Urine Output Management Overnight in the Home Setting.
ClinicalTrials.gov study NCT06666426. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical Decision Support With Continuous Glucose Monitoring Data for Managing Type 2 Diabetes
ClinicalTrials.gov study NCT07394075. IPD Sharing: YES. Countries: 0. Publications: 8.
A survey of sensor network use and data management among academic ecologists
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Canopy reflectance informs in-season malting barley nitrogen management: an ex-ante classification approach data
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Data from: Land management practices associated with house loss in wildfires
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Data from: Differential responses of herbivores and herbivory to management in temperate European beech
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Data from: Recreational harvest and incident-response management reduce human-carnivore conflicts in an anthropogenic landscape
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Data from: Comparing biocontrol and herbicide for managing an invasive non-native plant species: efficacy, non-target effects and secondary invasion
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.