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815 results for “Forecasting”
FUTURES land change forecasts in response to flooding in Charleston area, South Carolina (2019-2050)
<p>Climate-aware scenarios of FUTURES land change projections in Charleston area (Cherleston, Dorchester, Berkeley) for 2019-2050.</p> <p>Zipped folder <code>results</code> contains FUTURES v3 simulation runs for 3 counties in South Carolina, USA (Charleston, Berkeley, Dorchester) from 2019 to 2050. Included are five <em>climate-aware</em> scenarios (reactive, managed retreat, resist, polarized population, trapped population) and one scenario which does not take future climate conditions into account (<em>dynamic development</em>). Each folder contains 50 monte-carlo simulation results with <code>developed_seed_X.tif</code> and <code>adapted_seed_X.tif</code> where <code>X</code> goes from 1 to 50. Values of <code>developed_seed_X.tif</code> range from -31 to 31, where the positive values represent simulation step when an undeveloped pixel was developed and the negative values represent simulation step when a developed pixel was abandoned. Zero stands for initial development. For example, value -11 means the particular pixel was abandoned in 2030. Note that values of raster files in <code>Dynamic development</code> folder range from -1 to 31, where -1 means undeveloped and the rest is the same as above. Values of <code>adapted_seed_X.tif</code> range from 0 to 100, where 0 means trapped (flooded but no adaptation), the other values represent return period for which the pixel is adapted (e.g. 2-, 5-, 10-, 20-, 50- and 100-year flood). The files' CRS is Albers Conic Equal Area, NAD83(2011) datum.</p> <p>Additionally, we include <code>migration_matrix.csv</code> derived from IRS data, that contains probability values of moving from an origin county in the case study (row) to any other counties (columns).</p>
Forecasting wildlife movement with spatial capture-recapture
<ol> <li>Wildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developments in spatial capture-recapture modeling permit the integration of movement models that can estimate the movement of untagged and undetected individuals. Additionally, hidden Markov movement models provide a framework for forecasting individuals' movements, which may be valuable in the conservation of threatened species facing risks that vary across space and time.</li> <li>We describe maximum likelihood estimators for spatial capture–recapture models integrated with simple, biased, and correlated random walk movement models formulated as hidden Markov models. Additionally, we demonstrate how to forecast wildlife movement based on these models and hidden Markov model algorithms. We conducted a simulation study to test the performance of the models' abundance estimators and movement forecasts when fit to data simulated under different movement models. We also fit the models to spatial capture–recapture data collected on North Atlantic right whales off the Atlantic Coast of the southeastern United States.</li> <li>Random walk movement models improved abundance estimation and movement forecasts in our simulation study and received greater support from the data in the right whale case study than did activity center movement models.</li> <li>Forecasts of wildlife movement made under integrated spatial capture–recapture movement models will be most valuable when individuals have been observed recently, when sampling for individuals is extensive and efficient, and when the scale of individuals' movements is small relative to the scale of the study area and sampling process. </li> </ol>
FlashNet: AI framework for lightning forecasts
<p>This repository contains the code and a subset of the data used in:<br> "AI vs fully-deterministic algorithms: unraveling the dilemma for lightning prediction in the medium-range forecast horizon" by Mattia Cavaiola, Federico Cassola, Davide Secchetti, Francesco Ferrari, and Andrea Mazzino</p>
Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Beaverdam Reservoir forecasting data assimilation experiments using the FLARE (Forecasting Lake And Reservoir Ecosystems) system: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations and meteorological data, forecasts.zip contains forecast parquet files generated from the BVR FLARE DA experiment workflow, and scores.zip contains forecast skill metrics required for analysis. Within the forecasts and scores folders, there are four runs that were conducted with different parameter tuning and uncertainty quantification. The "all_UC" folder includes forecasts run with process, driver, parameter, and initial condition uncertainty quantification. The "IC_off" folder includes forecasts run without initial conditions uncertainty included (i.e., only process, driver, and parameter uncertainty). The "constant_bad_pars" folder includes forecasts run with constant parameters (but daily updating of initial conditions) that were not tuned for Beaverdam Reservoir before forecasts were generated. Finally, the "tuned_bad_pars" folder includes forecasts that were run with daily updating of initial conditions and parameters, but the parameters started out at random values that were not tuned for Beaverdam Reservoir.</p>
Forecasting live fuel moisture of Adenostema fasciculatum and its relationship to regional wildfire dynamics across southern California shrublands
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Data from: Biodiversity forecasting in natural plankton communities reveals temperature and biotic interactions as key predictors
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Huge ensembles part I design of ensemble weather forecasts with spherical Fourier neural operators; Huge ensembles part II properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators
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Data from: Estimating a physiologically-based threshold to oxygen and temperature from marine monitoring data reveals challenges and opportunities for forecasting distribution shifts
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Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
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Data from: Pathways to global-change effects on biodiversity: New opportunities for dynamically forecasting demography and species interactions
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Climate change scenarios forecast increased drought exposure for terrestrial vertebrates in the contiguous United States
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The past, present, and future of predator-prey interactions in a warming world: using species distribution modeling to forecast ectotherm-endotherm niche overlap
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The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France
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Forecasting wildlife movement with spatial capture-recapture
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Data from: A pattern-oriented simulation for forecasting species spread through time and space: A case study on an ecosystem engineer on the move
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Ecological forecasts for marine resource management during climate extremes
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Macrosystems EDDIE Module 8: Using Ecological Forecasts to Guide Decision-Making (Instructor Materials)
Because of increased variability in populations, communities, and ecosystems due to land use and climate change, there is a pressing need to know the future state of ecological systems across space and time. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to preemptively prepare for fluctuations in important ecosystem services. However, forecasts must be effectively designed and communicated to those who need them to make decisions in order to realize their potential for protecting natural resources. In this module, students will explore real ecological forecast visualizations, identify ways to represent uncertainty, make management decisions using forecast visualizations, and learn decision support techniques. Lastly, students customize a forecast visualization for a specific stakeholder's decision needs. The overarching goal of this module is for students to understand how forecasts are connected to decision-making of stakeholders, or the managers, policy-makers, and other members of society who use forecasts to inform decision-making. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials and the files necessary to teach the module. Readers are referred to the Zenodo data package (Woelmer et al. 2022; DOI: 10.5281/zenodo.7074674) for the R Shiny application code needed to run the module locally.
Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts (Instructor Materials)
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Forecast uncertainty is derived from multiple sources, including model parameters and driver data, among others. Knowing the uncertainty associated with a forecast enables forecast users to evaluate the forecast and make more informed decisions. This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown versi
Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts (Instructor Materials)
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 7: Using Data to Improve Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. To be useful for management, ecological forecasts need to be both accurate enough for managers to be able to rely on them for decision-making and include a representation of forecast uncertainty, so managers can properly interpret the probability of future events. To improve forecast accuracy, forecasts can be updated with observational data once they become available, a process known as data assimilation. Recent improvements in environmental sensor technology and an increase in the number of sensors deployed in ecosystems have increased the availability of data for assimilation to develop and improve forecasts for natural resource management. In this module, students will explore how assimilating data with different amounts of observation uncertainty and at different temporal frequencies affects forecasts of lake water quality, using data from the U.S. National Ecological Observatory Network (NEON). The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module7/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module7) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module7_R) of the module, and both code r
Systematic review of near-term ecological forecasting literature published between 1932 and 2020
This data publication includes results and code from a systematic review of near-term ecological forecasting literature. The study had two primary goals: (1) analyze the state of near-term ecological forecasting literature, and (2) compare forecast skill across ecosystems and variables. We began by conducting a Web of Science search for “forecast*” in the title, abstract, and keywords of all papers published in ecological journals, then screened all papers from this search to identify near-term ecological forecasts. We defined a near-term ecological forecast as future predictions of community, population, or biogeochemical variables ≤ 10 years from the forecast date. To more broadly survey the literature, we then searched all papers that cited or were cited by the near-term ecological forecasts we identified. We performed an in-depth review of all near-term ecological forecasting papers identified through this search process, and recorded forecast skill data for all papers that reported R or R2. Our results indicate that the rate of publication of near-term ecological forecasts is increasing over time and the field is becoming increasingly open and automated. Across published forecasts, we find that forecast skill decreases in predictable patterns and these patterns differ between forecast variables. This data publication includes three products from this analysis: (1) a database of all papers identified in the two searches, including our assessment of whether they included an ecological focal variable, included a forecast, and whether the forecast was near-term (≤10 years), (2) a matrix of all data collected on the near-term ecological forecasts we identified, and (3) a database of R2 values for papers that reported R or R2.
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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.