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74 results for “Environment Prediction”
Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe
<p>The data in this repository were used to conduct the analysis outlined in the following bioRxiv preprint:</p> <ul> <li>Sarah Hayes, Joe Hilton, Joaquin Mould-Quevedo, Christl Donnelly, Matthew Baylis, Liam Brierley (2025) "Ecology and environment predict spatially stratified risk of H5 highly pathogenic avian influenza clade 2.3.4.4b in wild birds across Europe" <em>bioRxiv</em> doi:10.1101/2024.07.17.603912</li> </ul> <p>The codes used for the analyses are available at https://github.com/sarahhayes/avian_flu_sdm/ </p> <p>The following lookup table can be used to cross-reference between the variable descriptions in Tables 1 and 2 of the preprint and the files in this repository:</p> <p> </p> <table> <tbody> <tr> <td> <h3> Variable description </h3> </td> <td> <h3> Filename </h3> </td> </tr> <tr> <td> Minimum elevation (metres above sea level) </td> <td> elevation_min_10kres.tif </td> </tr> <tr> <td> Maximum elevation (metres above sea level) </td> <td> elevation_max_10kres.tif </td> </tr> <tr> <td> Difference between minimum and maximum elevation </td> <td> elevation_diff_10kres.tif </td> </tr> <tr> <td> Modal elevation (metres above sea level) </td> <td> elevation_mode_10kres.tif </td> </tr> <tr> <td> Normalised Difference Vegetation Index (NDVI) </td> <td> ndvi_*_quart_2022_eco_rasts.tif </td> </tr> <tr> <td> Land cover </td> <td> landcover_output_full_2022_10kres.tif </td> </tr> <tr> <td> Distance to coast </td> <td> dist_to_coast_10kres.csv </td> </tr> <tr> <td> Distance to inland water </td> <td> dist_to_water_output_10kres.csv </td> </tr> <tr> <td> Relative humidity </td> <td> mean_relative_humidity_q*_10kres_eco_quarts.tif </td> </tr> <tr> <td>Seasonal weighted mean of the month-wise difference in the minimum temperature and maximum temperature (degrees Celsius) </td> <td> mean_diff_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal weighted mean of monthly mean temperatures (degrees Celsius) (Mean monthly temperature for each month calculated using: Mean temperature = Minimum temperature + diurnal range/2)</td> <td> mean_mean_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal temperature variation (degrees Celsius)<br>(Difference between the maximum and minimum of<br>mean monthly temperature values across months<br>majority-represented within the season)</td> <td> variation_in_quarterly_mean_temp_q*_eco_rasts.tif </td> </tr> <tr> <td> Precipitation </td> <td> mean_prec_*_quart_eco_rasts.tif </td> </tr> <tr> <td>Seasonal mean of daily zero-degree isotherm (metres<br>above sea level) </td> <td> isotherm_mean_q*_eco_rasts.tif </td> </tr> <tr> <td>Number of days the zerodegree isotherm was below 1 metre at midday at Coordinated Universal Time (UTC) </td> <td> isotherm_midday_days_below1_q*_eco_quarts.tif </td> </tr> <tr> <td> Chicken density </td> <td> chicken_density_2010_10kres.tif </td> </tr> <tr> <td> Duck density </td> <td> duck_density_2010_10kres.tif </td> </tr> <tr> <td> <em>Anatinae</em> (dabbling ducks) </td> <td> anatinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Anserinae</em> (swans and geese) </td> <td> anserinae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Ardeidae</em> (herons) </td> <td> ardeidae_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Arenaria/Calidris</em> (turnstones and sandpipers) </td> <td> arenaria_calidris_rast_eco_bds.tif </td> </tr> <tr> <td> <em>Aythyini</em> (diving ducks)</td> <td> aythyini_rast_eco_bds.tif</td> </tr> <tr> <td> Laridae (gulls) </td> <td> laridae_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding within 2m of water surface </td> <td> around_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage time spent feeding >2m below water surface </td> <td> below_surf_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet plants </td> <td> plant_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet scavenging </td> <td> scav_rast_eco_bds.tif </td> </tr> <tr> <td> Percentage diet endothermic vertebrates </td> <td> vend_rast_eco_bds.tif </td> </tr> <tr> <td> Congregative </td> <td> cong_rast_eco_bds.tif </td> </tr> <tr> <td> Migratory </td> <td> migr_rast_eco_bds.tif </td> </tr> <tr> <td> Below threshold phylogenetic distance to known host species </td> <td> host_dist_rast_eco_bds.tif </td> </tr> <tr> <td> Species richness </td> <td> species_richness_rast_eco_bds.tif </td> </tr> </tbody> </table>
Distributed Predictive Drone Swarms in Cluttered Environments
<p>This folder contains data, videos, and supplementary material for the article titled "Distributed Predictive Drone Swarms in Cluttered Environments".</p> <p>In the article, we present a Distributed Model Predictive Control (DMPC) algorithm for drone swarm navigation in two types of cluttered environments, i.e., a forest and a funnel-like environment. <br> </p> <p>The material in `zenodo_upload` is organized as follows.<br> 1. a data folder, with the logs of simulation and hardware experiments;<br> 2. an analysis folder, with Matlab scripts that analyze the logs in the data folder;<br> 3. a plotting folder, with Matlab functions used by the analysis scripts;<br> 4. an mp4 video file, on simulation and hardware experiments;<br> 5. a pdf, with supplementary materials.<br> <br> The `qp_swarm` folder contains MATLAB code for simulation experiments.</p>
Spatial predictions of suitable environments for palsas and peat plateaus in the Northern Hemisphere for recent and future periods
<p>Here we provide raster files of suitable environments for palsas and peat plateaus in the Northern Hemisphere. These files are results of a scientific study by Könönen et al. (2022, preprint). Files are provided in TIFF-format, and they describe the occurrence probability of the suitable environments for palsas and peat plateaus.</p> <p> </p> <p>Könönen, O. H., Karjalainen, O., Aalto, J., Luoto, M., and Hjort, J.: Environmental spaces for palsas and peat plateaus are disappearing at a circumpolar scale, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2022-135, in review, 2022.</p>
Predictive Control of Aerial Swarms in Cluttered Environments
<p>This repository contains all data that has been used to produce the results contained in the submission to Nature Machine Intelligence titled "Predictive Control of Aerial Swarms in Cluttered Environments". It also contains the code necessary for plotting simulation and hardware experimental data.</p> <p>For the code used to run simulation and hardware experiments, please visit <a href="http://doi.org/10.5281/zenodo.4379503">doi.org/10.5281/zenodo.4379503</a>.</p>
How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design
<p>Extra-organismal DNA (eoDNA) from material left behind by organisms (non-invasive DNA: e.g., faeces, hair) or from environmental samples (eDNA: e.g., water, soil) is a valuable source of genetic information. However, the relatively low quality and quantity of eoDNA, which can be further degraded by environmental factors, results in reduced amplification and sequencing success. This is often compensated for through cost- and time-intensive replications of genotyping/sequencing procedures. Therefore, system- and site-specific quantifications of environmental degradation are needed to maximize sampling efficiency (e.g., fewer replicates, shorter sampling durations), and to improve species detection and abundance estimates. Using ten environmentally diverse bat roosts as a case study, we developed a robust modelling pipeline to quantify the environmental factors degrading eoDNA, predict eoDNA quality, and estimate sampling-site-specific ideal exposure duration. Maximum humidity was the strongest eoDNA-degrading factor, followed by exposure duration and then maximum temperature. We also found a positive effect when hottest days occurred later. The strength of this effect fell between the strength of the effects of exposure duration and maximum temperature. With those predictors and information on sampling period (before or after offspring were born), we reliably predicted mean eoDNA quality per sampling visit at new sites with a mean squared error of 0.0349. Site-specific simulations revealed that reducing exposure duration to 2-8 days could substantially improve eoDNA quality for future sampling. Our pipeline identified high humidity and temperature as strong drivers of eoDNA degradation even in the absence of rain and direct sunlight. Furthermore, we outline the pipeline's utility for other systems and study goals, such as estimating sample age, improving eDNA-based species detection, and increasing the accuracy of abundance estimates.</p>
Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments
<p>A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from−0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.</p>
Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning: Cleaned Data
<p>The included files and script are to allow for reconstruction of the data directory and cleaned data used in "Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning" ( https://doi.org/10.1101/2022.07.29.502051 ). Code used is available at 10.5281/zenodo.7401113 .</p> <table> <tbody> <tr> <th>Filename</th> <th>Description</th> </tr> <tr> <td>interim.tar.gz</td> <td>Contains site grouping dictonary</td> </tr> <tr> <td>processed.tar.gz</td> <td>Processed data</td> </tr> <tr> <td>raw.tar.gz</td> <td>Input data</td> </tr> <tr> <td>SetupInstructions.sh</td> <td>Bash script to prepare folders and unzipped data expected by code in 10.5281/zenodo.7401113</td> </tr> <tr> <td>SetupInstructions.txt</td> <td>Instructions for unzipping the data</td> </tr> <tr> <td>Train_Test_Split_Reference_Phenotypes.csv</td> <td>Reference spreadsheet to allow for easily exploring training and test set groupings</td> </tr> </tbody> </table> <ul> </ul> <p>This work was supported through funding from the USDA Agricultural Research Service, ARS project number 5070-21000-041-000-D. Raw data provided by the [Genomes to Field Initiative](https://www.genomes2fields.org/) and the [Daymet database](https://daymet.ornl.gov/).</p>
Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"
<p>Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions.</p> <p>As soon as the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here. </p> <p>The .zip file contains the following folders:</p> <p>1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution</p> <p>2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil attributes prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil attributes prediction</p>
How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design
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Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments
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Brain size predicts bees’ tolerance to urban environments
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Data from: Phenotype-environment matching predicts both positive and negative effects of intraspecific variation
Natural populations can vary considerably in their genotypic and/or phenotypic diversity. Differences in this intraspecific diversity can have important consequences for contemporary ecological dynamics, but the direction and magnitude of these effects appear inconsistent across studies and systems. Here we proposed and tested the hypothesis that context-dependent ecological effects of altering phenotypic variance are predictable and arise from the relationship between a population's mean phenotype and the local environmental optimum. By factorially manipulating the mean and variance of a key host trait in environments with and without a lethal parasite, we demonstrate that increasing phenotypic variance can have beneficial effects for host populations (e.g. smaller disease epidemics), but only when the population's initial phenotype was poorly-matched to the local environment. When phenotypes were initially well-suited to environmental conditions, in contrast, greater phenotypic variance led to larger disease epidemics. Significant reductions in individual susceptibility occurred in both contexts over time, but the mechanisms leading to those reductions differed; strong selection was caused by either a 'suboptimal' trait mean and insufficient trait variance, or a 'near-optimal' trait mean and too much trait variance. Increasing intraspecific variation is clearly not always beneficial for populations, instead producing predictable ecological and evolutionary effects that depend on environmental context and biological interactions.
Data from: Genotyping by sequencing and genome–environment associations in wild common bean predict widespread divergent adaptation to drought
Drought will reduce global crop production by >10% in 2050 substantially worsening global malnutrition. Breeding for resistance to drought will require accessing crop genetic diversity found in the wild accessions from the driest high stress ecosystems. Genome–environment associations in crop wild relatives reveal natural adaptation, and therefore can be used to identify adaptive variation. We explored this approach in the food crop Phaseolus vulgaris L., characterizing 86 geo-referenced wild accessions using Genotyping by Sequencing (GBS) to discover single-nucleotide-polymorphisms (SNPs). The wild beans represented Mesoamerica, Guatemala, Colombia, Ecuador/Northern Peru and Andean groupings. We found high polymorphism with a total of 22,845 SNPs across the 86 accessions loci that confirmed genetic relationships for the groups. As a second objective, we quantified allelic associations with a bioclimatic-based drought index using 10 different statistical models that accounted for population structure. Based on the optimum model, 115 SNPs in 90 regions, widespread in all 11 common bean chromosomes, were associated with the bioclimatic-based drought index. A gene coding for an Ankyrin repeat-containing protein and a phototropic-responsive NPH3 gene were identified as potential candidates. Genomic windows of 1Mb containing associated SNPs had more positive Tajima's D scores than windows without associated markers. This indicates that adaptation to drought, as estimated by bioclimatic variables, has been under natural divergent selection, suggesting that drought tolerance may be favorable under dry conditions but harmful in humid conditions. Our work exemplifies that genomic signatures of adaptation are useful for germplasm characterization, potentially enhancing future marker-assisted selection and crop improvement.
Prediction of Compounds in Different Local SAR Environments using ECP
<p>SD files of 15 data sets reported in the manuscript are uploaded. Each data set is represented by its CHEMBL Target ID. The file format is provided in the file 'description.txt'.</p>
Harnessing interactions between traits and the environment to improve predictions of ecosystem functioning
<p>The data correspond to the manuscript entitled "Harnessing interactions between traits and the environment to improve predictions of ecosystem functioning". Data needed to reproduce the figure 2 about springtails colonisation of defaunated soil blocks, from two different environments (forest or meadow), with the springtail species classified according to the supposed dispersal ability. S<span>pecies with long legs and antennae, a well-developed jumping apparatus (furcula), and a complete visual apparatus were considered to be able to disperse more rapidly on their own and were thus categorized as “fast” dispersers. Other species with reduced locomotor and vision organs were categorized as “slow” dispersers </span><span>(Ponge <em>et al.</em> 2006a)</span><span>. </span></p> <p><span>The "number of individuals" corresponds to the number of individuals found in each defaunated soil block after one week in the initial data set (Auclarc et al., 2009).</span></p> <p><span>Ponge J-F, Dubs F, Gillet S, et al. 2006a. Decreased biodiversity in soil springtail communities: the importance of dispersal and landuse history in heterogeneous landscapes. </span>Soil Biol Biochem 38: 1158–61.</p> <p><span>Auclerc A, Ponge JF, Barot S, and Dubs F. 2009. Experimental assessment of habitat preference and dispersal ability of soil springtails. </span>Soil Biol Biochem 41: 1596–604.</p>
Testing the predictability of morphological evolution in contrasting thermal environments
<p>Gaining the ability to predict population responses to climate change is a pressing concern. Using a 'natural experiment', we show that testing for divergent evolution in wild populations from contrasting thermal environments provides a powerful approach, and likely an enhanced predictive power for responses to climate change. Specifically, we used a unique study system in Iceland, where freshwater populations of threespine sticklebacks (<em>Gasterosteus aculeatus</em>) are found in waters warmed by geothermal activity, adjacent to populations in ambient-temperature water. We focused on morphological traits across six pairs from warm and cold habitats. We found that fish from warm habitats tended to have a deeper mid-body, a sub-terminally orientated jaw, steeper craniofacial profile, and deeper caudal region relative to fish from cold habitats. Our common garden experiment showed that most of these differences were heritable. Population age did not appear to influence the magnitude or type of thermal divergence, but similar types of divergence between thermal habitats were more prevalent across allopatric than sympatric population pairs. These findings suggest that morphological divergence in response to thermal habitat, despite being relatively complex and multivariate, are predictable to a degree<a name="_Hlk345127"></a>. Our data also suggests that the potential for migration of individuals between different thermal habitats may enhance non-parallel evolution and reduce our ability to predict responses to climate change.</p>
Data from: Phenotype-environment matching predicts both positive and negative effects of intraspecific variation
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Testing the predictability of morphological evolution in contrasting thermal environments
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Local dominance predicts foraging decisions in a changing environment
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Data from: Genotyping by sequencing and genome–environment associations in wild common bean predict widespread divergent adaptation to drought
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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.