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1,121 results for “weathering”
Temperature and rainfall datasets for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting"
<p>This page includes spatiotemporal datasets used in the paper <a href="https://doi.org/10.1016/j.neucom.2020.09.060">STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting.</a></p> <p>ARIMA and deep learning models use datasets, as follow:</p> <ul> <li>ARIMA models</li> </ul> <p>baseline-chirps-1981-2019.nc (rainfall data)<br> baseline-ucar-1979-2015.nc (temperature data)</p> <ul> <li>Deep learning models:</li> </ul> <p><em>5-step ahead:</em></p> <p>dataset-chirps-1981-2019-seq5-ystep5.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep5.nc (temperature data)</p> <p><em>15-step ahead:</em></p> <p>dataset-chirps-1981-2019-seq5-ystep15.nc (rainfall data)<br> dataset-ucar-1979-2015-seq5-ystep15.nc (temperature data)</p>
Observed and WRF-simulated air temperature and wind speed at the Czech Hydrometeorological Institute weather stations Lučina, Lysá hora and Olomouc
<p>The dataset contains two csv files with observed 2-m air temperature and 10-m wind speed data at Lučina, Lysá hora and Olomouc meteorological stations in the Czech Republic and analogical time series produced by the Weather Research and Forecasting (WRF) model. The dataset covers a period of 27 October 2010, 01:00 UTC to 01 November 2010, 00:00 UTC. WRF output is given for three model configurations:</p> <p>1) QNSE boundary layer scheme</p> <p>2) 3DTKE boundary layer scheme with Revised MM5 surface layer scheme</p> <p>3) 3DTKE boundary layer scheme with MYNN surface layer scheme</p>
Geochemistry of a weathering profile and natural waters in the upland Mediterranean catchment of Sapine, Mt-Lozère, southern France
<p>This publication reports geochemical data from an instrumented catchment of the Service National d’Observation “Observatoire Hydro-Météorologique Cévennes-Vivarais” (OHMCV) and of the National Research Infrastructure “Observatoires de la Zone Critique: Applications et Recherche” (OZCAR) of the French “Centre National de la Recherche Scientifique” (CNRS).</p> <p>The presented data are originally contained in Kuessner, M.L. (2018). The interplay between chemical weathering and hydrology in the Critical Zone - insight from trace elements and lithium isotopes [Doctoral dissertation, Institut de Physique du Globe de Paris]</p> <p>Contents</p> <p>Table SC1. Analyses of soil samples in Sapine Creek</p> <p>Table SC2. Analyses of rock samples in Sapine Creek</p> <p>Table SC3. Analyses of rain samples in Sapine Creek and Cloutasse catchments</p> <p>Table SC4. Analyses of stream samples in Sapine Creek</p> <p>Table SC5. Analytical limits for quadrupole ICP-MS measurements</p> <p>Table SC6. Analyses of reference materials for isotope measurements</p>
Dataset for "Effects of weather and climate on fluctuations of grain prices in southwestern Bohemia, 1725–1824 CE"
<p><span>This deposit contains three .xlsx files.</span></p> <p><span>The file „01_prices_Sušice_1725-1824“ contains two sheets with mean annual prices (in Lower Austrian <em>měřice</em>) of four cereals (wheat, rye, barley and oats) in Sušice for the period 1725–1824. On the first sheet are compiled series, on the second detrended series (using high-pass filter).</span></p> <p><span>The file „02_monthly_prices_1725-1738“ contains four sheets with mean monthly prices (in Lower Austrian <em>měřice</em>) of four cereals in Sušice from July 1725 to July 1738 (July 1737 is missing). Sheets represent individual cereals – wheat, rye, barley and oats.</span></p> <p><span>The file „03_climate_char_1725-1824“ contains two sheets with mean seasonal (DJF, MAM and JJA) temperature, precipitation and scPDSI expressed in deviations relative to the 1961–1990 reference period. On the first sheet are original series, on the second detrended series (using high-pass filter). The original series are reconstructed temperatures for central Europe (Dobrovolný et al., 2010), reconstructed precipitation for the Czech Lands (Dobrovolný et al., 2015) and both of them were used for creation of scPDSI series (Brázdil et al., 2016).</span></p>
Dataset for "Demographic yearbooks as a source of weather-related fatalities: the Czech Republic, 1919–2022"
<p><span>This deposit contains three .xlsx files.</span></p> <p><span>The file „01_fatalities_1919-2022“ contains annual numbers of fatalities (males, females and sum) for individual categories of external death causes attributed to weather and natural extremes, excerpted from demographic yearbooks for the Czech Republic for the period 1919–2022. </span></p> <p><span>The file „02_age_categories_1931-2022“ contains eight sheets with annual numbers of weather-related fatalities in the Czech Republic in the period 1931–2022 for eight age categories and for males and females separately. Sheets represent individual categories of death causes – Cold, Heat, Lightning, Natural hazards, Fall on ice or snow, Air pressure. Heat and Natural hazards are divided into two sheets – one with summarized numbers and one with numbers for individual sub-categories.</span></p> <p><span>The file „03_clima_factors“ contains mean temperature of January–February (Brázdil et al., 2012, extended) and mean annual number of days with a thunderstorm in the Czech Republic for the period 1919–2022 and mean temperature of winter season (DJF) in the Czech Republic for the period 1986/1987–2021/2022 (Brázdil et al., 2012, extended).</span></p>
Weekly plots of Great Britain's half-hourly electrical system weather dependent generation, net imports and overall demand from 2008-11-10
<p>Plots that show the electrical system transition of Great Britain, they were created to form the individual frames for a video of the transition.</p>
Zonal Statistics of Weather Indicators for Brazilian Municipalities from the BR-DWGD Project
<p>This dataset presents daily weather indicators for Brazilian municipalities computed with zonal statistics using the data from the <a href="https://sites.google.com/site/alexandrecandidoxavierufes/brazilian-daily-weather-gridded-data" target="_blank" rel="noopener">BR-DWGD project</a> (version 3.2.3), from 1961-01-01 to 2024-03-20.</p> <p> </p> <table> <tbody> <tr> <td>File</td> <td>Indicator</td> <td>Unit</td> </tr> <tr> <td>pr_3.2.3.parquet</td> <td>Precipitation</td> <td>mm</td> </tr> <tr> <td>ETo_3.2.3.parquet</td> <td>Evapotranspiration</td> <td>mm</td> </tr> <tr> <td>Tmax_3.2.3.parquet</td> <td>Maximum temperature</td> <td>°C</td> </tr> <tr> <td>Tmin_3.2.3.parquet</td> <td>Minimum temperature</td> <td>°C</td> </tr> <tr> <td>Rs_3.2.3.parquet</td> <td>Solar radiation</td> <td>MJm-2</td> </tr> <tr> <td>u2_3.2.3.parquet</td> <td>Wind speed at 2 m height</td> <td>m/s</td> </tr> <tr> <td>RH_3.2.3.parquet</td> <td>Relative humidity</td> <td>%</td> </tr> </tbody> </table> <p>The methodology to compute the zonal statistics follows <a href="https://doi.org/10.1017/eds.2024.3" target="_blank" rel="noopener">https://doi.org/10.1017/eds.2024.3</a> .</p>
Daily weather data averages for Germany aggregated over official weather stations
<p>Daily data averaged across Germany for the period of 2016-01-01 till 2021-06-27:</p> <p><strong>temperature_mean: </strong>mean daily temperature in degree Celsius averaged across all weather stations in Germany.</p> <p><strong>temperature_max:</strong> maximum daily temperature in degree Celsius averaged across all weather stations in Germany.</p> <p><strong>precipitation:</strong> daily precipitation sum in millimeter (equals liter per square meter) averaged across all weather stations in Germany.</p> <p><strong>sunshine: </strong>sunshine duration per day averaged across all weather stations in Germany.</p> <p> </p> <p> </p> <p>gemittelte Werte basierende auf Daten des Deutschen Wetterdiensts, Vermessungsverwaltungen der Länder und BKG (https://gdz.bkg.bund.de/)</p>
Schools Weather and Air Quality (SWAQ) –Metadata – Urban Network, Sydney (NSW)
<p>Schools Weather and Air Quality (SWAQ) is a citizen science project funded by the Department of Industry, Innovation and Science as part of its Inspiring Australia - Citizen Engagement Program. SWAQ is equipping public schools across Sydney with research-grade meteorology and air quality sensors, enabling students to collect and analyse research quality data through curriculum-aligned classroom activities.</p> <p>The network includes twelve automatic weather stations and seven automatic air quality stations, stretched from -33.5995° to -34.0424° latitude and from 150.6918° to 151.2706° longitude. The average spacing is 10.2 km and the average installation height is 2.5 m above ground level. Six meteorological parameters (dry-bulb temperature, relative humidity, barometric pressure, rain, wind speed, and wind direction) and six air pollutants (SO2, NO2, CO, O3, PM2.5, and PM10) are recorded via Vaisala WXT 536 and Vaisala AQT 420 with a 20 minutes sampling frequency. </p> <p>SWAQ data provides urban canopy layer observations of the intra-urban heterogeneity and inter-parameter dependency of all major urban climate and air quality variables, valuable across diverse urban disciplines. SWAQ stations are located where there are gaps in existing government networks, and focus on Sydney’s western suburbs, where the highest urbanization rate is taking place, to better inform future urban planning. QC procedures are designed to ensure observations of extreme episodes are not excluded. Beyond research purposes, SWAQ is a citizen-centered network, conceived to promote valuable STEM (science, technology, engineering, mathematics) skills among citizens and students.</p> <p>This collection includes the metadata files for all SWAQ stations, in pdf. Metadata describe the site (type, geographic coordinates, elevation, orographic setting, representativeness, local climatic zone, dominant land use, percent land cover, mean tree and building heights, proximity to water/heat and pollutants sources/sinks, estimation of Davenport Roughness, traffic density, sky view factor), and the instrumentation (variables, models, manufacturers, calibration and installation dates). Site characteristics are described at three radial scales: 20 km, 500 m, and 50 m. Graphical representations include: satellite images, street-view maps, cardinal direction photographs, panoramic photos, and close-up photos of sensors, solar panels, and connections. Optimum site allocation was determined by undertaking a multi-criteria weighted overlay analysis to ensure data representativeness and quality. All SWAQ sensors are installed:</p> <ul> <li>in homogenous urban regions, without sections of anomalous variation in the regional urban makeup and aspect-ratio, and without large, concentrated heat/pollution sources or sinks;</li> <li>in areas falling into the WMO Class 4 with no electromagnetic sources that could have distorted the transmission;</li> <li>at a constant height of 2 - 3.5 m above ground level.</li> </ul> <p>The actual data is available from the Australian Terrestrial Ecosystem Research Network (TERN) <a href="https://https://portal.tern.org.au/schools-weather-air-sydney-nsw/22077">data portal</a> and is regularly updated. The data available from TERN has undergone a rigorous quality check routine before upload.</p> <ul> <li>Calibration: Sensors and gateways are calibrated and tested by Vaisalain controlled conditions </li> <li>Quality Assurance: Annual maintenance log</li> <li>Quality control: continuity tests, fixed range tests (on both physical and instrumental limits), dynamic range and step tests (both performed on a monthly basis), internal consistency tests (on known atmospheric relations) and persistence tests. </li> </ul> <p>The files are in csv format. On the <a href="https://www.swaq.org.au">SWAQ website</a> a non quality controlled subset is available for educational purposes only.</p> <p>This project was funded by an Australian Government Department of Industry, Innovation and Science, Inspiring Australia – Science Engagement Program: Citizen Science Grants (CSG56028).</p> <p>It was also part of the Centre of Excellence for Climate Extremes research project "Attribution & Risk".</p> <p>More information is available in the readme file, in particular a full list of the variables and a legend for the quality flags used.</p>
Inclement weather forces stopovers and prevents migratory progress for obligate soaring migrants
<p>Data from: Mallon, JM, KL Bildstein, and WF Fagan. 2021. Inclement weather forces stopovers and prevents migratory progress for obligate soaring migrants. Movement Ecology</p> <p>These data include only migrations and are annotated with stopovers. See metadata for full information.</p> <p>Data modified from Bildstein KL and Barber D. 2021. Movebank Data Repository. https://doi.org/10.5441/001/1.f3qt46r2.</p> <p> </p>
Dataset for "Beating 1 Sievert: Optimal Radiation Shielding of Astronauts on a Mission to Mars" publication in Space Weather journal
<p>Datasets in .fig Matlab format and figures in .jpg format published in Space Weather journal</p> <p>effectiveDoseRF.mat contains the effective dose "response functions" and an example (how2useDoseResponceFunctions.m) of how to use them to assess GCR dose.</p>
Oikolab Weather Dataset
<p>This dataset was kindly provided by OikoLab (https://oikolab.com). It contains eight time series representing the hourly climate data nearby Monash University, Clayton, Victoria, Australia from 2010-01-01 to 2021-05-31. The climate data include temperature (C), dewpoint temperature (C), wind speed (m/s), mean sea level pressure (Pa), relative humidity (0-1), surface solar radiation (W/m^2), surface thermal radiation (W/m^2) and total cloud cover (0-1).<br> </p>
Supply Chain Shocks due to extreme weather events
<p>Projected supply chain shocks due to extreme weather events measured in annual percentage change in a country-sector's export activity compared to the baseline period</p>
Simulated daily weather dynamics and gross primary production in 3 locations for 100,000 years
<p>IMPORTANT NOTE: The data in version 1 of this record, due to an error of units in the precipitation, had a non-physical vegetation growth and gross primary production. This has been fixed in version 2 of the record/dataset. Further, version 2 of the dataset contains 3 locations because the sites called "Grassland" and "Temperate" site produced the same type of vegetation (just grasses) in version 2, that contains therefore only a "Temperate" site.</p> <p>-------------------------------------------------</p> <p>The dataset reports daily temperature, precipitation, radiation and gross primary production in 3 different geographic locations (denoted as Temperate, Boreal and Tropical), representative of different climates and vegetation distributions, for 100,000 years. Each of the .nc files contains the dataset corresponding to one particular site.</p> <p>The weather data was produced using the AWE-GEN stochastic weather generator model ( Fatichi et al., Water Resources, 34(4):448–467 (2011) ). Vegetation dynamics and gross primary production are simulated via the dynamic global vegetation model LPX-Bern v1.4 ( Lienert and Joos, Biogeosciences, 15(9):2909–2930 (2018) ). The foliar projective cover is also reported at an annual scale.</p> <p> </p>
Weather prediction dataset
<p>Dataset created for machine learning and deep learning training and teaching purposes.<br> It can, for instance, be used for classification, regression, and forecasting tasks.<br> Complex enough to demonstrate realistic issues such as overfitting and unbalanced data, while still remaining intuitively accessible.</p> <p><strong>Description and units of weather features:</strong></p> <p>Data includes the following features/variables for several European cities:</p> <table> <tbody> <tr> <td>Feature (type)</td> <td>Column name</td> <td>Description</td> <td>Physical Unit</td> </tr> <tr> <td>mean temperature</td> <td>_temp_mean</td> <td>mean daily temperature</td> <td>in 1 °C</td> </tr> <tr> <td>max temperature</td> <td>_temp_max</td> <td>max daily temperature</td> <td>in 1 °C</td> </tr> <tr> <td>min temperature</td> <td>_temp_min</td> <td>min daily temperature</td> <td>in 1 °C</td> </tr> <tr> <td>cloud_cover</td> <td>_cloud_cover</td> <td>cloud cover</td> <td>oktas</td> </tr> <tr> <td>global_radiation</td> <td>_global_radiation</td> <td>global radiation</td> <td>in 100 W/m2</td> </tr> <tr> <td>humidity</td> <td>_humidity</td> <td>humidity</td> <td>in 1 %</td> </tr> <tr> <td>pressure</td> <td>_pressure</td> <td>pressure</td> <td>in 1000 hPa</td> </tr> <tr> <td>precipitation</td> <td>_precipitation</td> <td>daily precipitation</td> <td>in 10 mm</td> </tr> <tr> <td>sunshine</td> <td>_sunshine</td> <td>sunshine hours</td> <td>in 0.1 hours</td> </tr> <tr> <td>wind_speed</td> <td>_wind_gust</td> <td>wind gust</td> <td>in 1 m/s</td> </tr> <tr> <td>wind_gust</td> <td>_wind_speed</td> <td>wind speed</td> <td>in 1 m/s</td> </tr> </tbody> </table> <p><strong>File descriptions</strong></p> <ul> <li><code>weather_prediction_dataset.csv</code> - Main data file, tabular data, comma-separated CSV. Contains the data for different weather features (daily observations, see below for more details) for 18 European cities or places through the years 2000 to 2010.</li> <li><code>weather_prediction_picnic_labels.csv</code> - Optional data to be used as potential labels for classification tasks. Contains booleans to characterize the daily weather conditions as suitable for a picnic (True) or not (False) for all 18 locations in the dataset.</li> <li><code>weather_prediction_dataset_map.png</code>- Simple map showing all 18 locations in Europe.</li> <li><code>metadata.txt</code> - Further information on the dataset, the data processing, and conversion, as well as the description and units of all weather features.</li> </ul> <p> </p> <p>ORIGINAL DATA TAKEN FROM:</p> <p>EUROPEAN CLIMATE ASSESSMENT & DATASET (ECA&D), file created on 22-04-2021<br> THESE DATA CAN BE USED FREELY PROVIDED THAT THE FOLLOWING SOURCE IS ACKNOWLEDGED:</p> <p>Klein Tank, A.M.G. and Coauthors, 2002. Daily dataset of 20th-century surface<br> air temperature and precipitation series for the European Climate Assessment.<br> Int. J. of Climatol., 22, 1441-1453.<br> Data and metadata available at http://www.ecad.eu</p> <p>For more information see metadata.txt file.<br> The dataset has also been presented at the Teaching Machine Learning Workshop at ECML 2022: https://teaching-ml.github.io/2022/.</p> <p>The Python code used to create the weather prediction dataset from the ECA&D data can be found on GitHub: https://github.com/florian-huber/weather_prediction_dataset<br> (this repository also contains Jupyter notebooks with teaching examples)</p> <p>Versions:</p> <ul> <li><strong>v5</strong>: updated metadata.txt file.</li> <li><strong>v4</strong>: to be more future proof in times of climate change/crisis --> "BBQ weather" prediction is now "picnic weather" prediction. Data itself remains unchanged.</li> <li><strong>v3</strong>: added "light" version of the dataset with less features (only 11 locations and fewer variables, reduction from 163 to 89 features) --> This is meant to be used if training times for hands-on session is becoming an issues</li> <li><strong>v2</strong>: now also contains additional `BBQ_weather` labels, the dataset itself has not changed between versions v1 and v2</li> </ul> <p> </p>
Dataset of Machine Learning forecasted VTEC from paper: Uncertainty Quantification for Machine Learning-based Ionosphere and Space Weather Forecasting
<p>The *csv files contain forecasted one-day-ahead Vertical Total Electron Content (VTEC), consisting of the mean/median VTEC values and the upper and lower VTEC bounds of the 95% confidence intervals of 4 models based on machine learning for test data.</p> <p>The first part of the *csv file name corresponds to the type of model: SE stands for the super-ensemble VTEC model, QGB stands for the quantile gradient boosting VTEC model, BNN1 stands for the Bayesian neural network VTEC model, and BNN2 stands for the Bayesian neural network with negative log-likelihood (NLL) loss VTEC model. The second part of the file name refers to the geographic location of the VTEC points for which the forecast is performed, i.e., 10E70N for 10 degree of longitude and 70 degree of latitude, 10E40N for 10 degree of longitude and 40 degree of latitude, and 10E10N for 10 degree of longitude and 10 degree of latitude. The last part of the file name corresponds to the test year, i.e., year 2017.</p> <p>The SE_*_2017.csv file consists of 14 columns. The index column ("Date-time") is expressed in Coordinated Universal Time (UTC) as YYYY-MM-DD. Columns 1-3 contain the VTEC forecast results of Random Forest (RF) trained on three data subsets; columns 4-6 contain the VTEC forecast results of Adaptive Boosting (AB) trained on three data subsets; columns 7-9 contain the VTEC forecast results of Gradient Boosting (XGBoost) trained on three data subsets. Column 10 ("Mean") represents the mean of columns 1-9, i.e., the ensemble mean; column 11 ("Std") represents the standard deviation of columns 1-9, i.e., the ensemble spread; columns 12 ("UB") and 13 ("LB") contain the upper and lower bounds of the 95% confidence interval of VTEC, respectively; and column 14 contains the Global Ionosphere Maps (GIM) values of CODE, i.e., the ground-truth in this study.</p> <p>The QGB_*_2017.csv file consists of 4 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Median") contains the median VTEC forecast, column 2 ("LB") contains the lower VTEC bound of the 95% confidence interval, column 3 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 4 contains the GIM values of CODE, i.e., the ground-truth in this study.</p> <p>The BNN*_2017.csv file consists of 5 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Mean") contains the mean VTEC forecast, column 2 ("Std") contains the standard deviation, column 3 contains GIM values of CODE, i.e., ground-truth in this study; column 4 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 5 ("LB") contains the lower VTEC bound of the 95% confidence interval.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Contact</p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p>If you have any questions regarding these data, please contact:</p> <p>Randa Natras</p> <p>Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)</p> <p>Technical University of Munich</p> <p>Arcisstraße 21</p> <p>80333 München</p> <p>randa.natras@tum.de</p>
Zonal Statistics of Weather Indicators for Brazilian Municipalities from the TerraClimate Project
<p>This dataset contains 14 parquet-format files with monthly data.</p> <table align="center"> <tbody> <tr> <td>File</td> <td>Indicator</td> <td>Unit</td> </tr> <tr> <td>aet.parquet</td> <td>Actual Evapotranspiration</td> <td>mm</td> </tr> <tr> <td>def.parquet</td> <td>Climate Water Deficit</td> <td>mm</td> </tr> <tr> <td>pdsi.parquet</td> <td>Palmer Drought Severity Index (PDSI)</td> <td>unitless</td> </tr> <tr> <td>pet.parquet</td> <td>Precipitation</td> <td>mm</td> </tr> <tr> <td>ppt.parquet</td> <td>Potential evapotranspiration</td> <td>mm</td> </tr> <tr> <td>q.parquet</td> <td>Runoff</td> <td>mm</td> </tr> <tr> <td>soil.parquet</td> <td>Soil Moisture</td> <td>mm</td> </tr> <tr> <td>srad.parquet</td> <td>Downward surface shortwave radiation</td> <td>W/m2</td> </tr> <tr> <td>swe.parquet</td> <td>Snow water equivalent</td> <td>mm</td> </tr> <tr> <td>tmax.parquet</td> <td>Maximun Temperature</td> <td>°C</td> </tr> <tr> <td>tmin.parquet</td> <td>Minimum Temperature</td> <td>°C</td> </tr> <tr> <td>vap.parquet</td> <td>Vapor pressure</td> <td>kPa</td> </tr> <tr> <td>vpd.parquet</td> <td>Vapor Pressure Deficit</td> <td>kpq</td> </tr> <tr> <td>ws.parquet</td> <td>Wind speed</td> <td>m/s</td> </tr> </tbody> </table> <p> </p>
Temporal variation in spider trophic interactions is explained by the influence of weather on prey communities, web building and prey choice
<p>Materials and Methods</p> <p><em>Fieldwork</em><em> and sample processing</em></p> <p>Field collection and sample processing has been described previously by Cuff, Tercel, et al., (2022), but is briefly described in Supplementary Information 1. In short, money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were collected from occupied webs and the ground in barley fields between April and September 2018. Linyphiids occupying webs (n = 78) were prioritised for collection, but ground-active linyphiid and lycosid spiders were also collected. For each linyphiid taken from a web, the height of the web from the ground (mm) and its approximate dimensions were recorded, the latter calculated as approximate web area (mm<sup>2</sup>). To obtain data on local prey density, ground and crop stems were suction sampled using a ‘G-vac’ for approximately 30 seconds at each 4 m<sup>2</sup> quadrat from which spiders were collected. Extraction, amplification and sequencing of DNA, and bioinformatic analysis is described by Cuff, Tercel, et al. (2022) and Drake et al. (2022), and is also detailed in Supplementary Information 2. Amplification was carried out using two complementary PCR primer pairs: one targeting invertebrates generally, and one intended to exclude amplification of spider DNA to reduce the prevalence of ‘host’ reads in the data output (Cuff et al. 2023). Amplicons were sequenced via Illumina MiSeq V3 with 2x300 bp paired-end reads. The resultant sequencing read counts were converted to presence-absence data of each detected prey taxon in each individual spider. Given the prevalence of sequencing reads associated with each spider analysed and the impossibility of disentangling these from detections of intraspecific predation (i.e., cannibalism), all such reads were removed (Cuff et al. 2023), although intrageneric and intrafamilial predation were still detected.</p> <p> </p> <p><em>Weather data</em></p> <p>Weather data were taken from publicly available reports from the Cardiff Airport weather station (6.6 km from the study site) via “Wunderground” (Wunderground, 2020), to represent local weather conditions. This does not necessarily reflect smaller-scale effects (e.g., microclimate-scale; Bell, 2014; Holtzer et al., 1988), but the timescale of detection for dietary metabarcoding reduces the value of that resolution given that spiders may forage across multiple microclimates. We collated data from 1<sup>st</sup> January 2018 to 17<sup>th</sup> September 2018 (the last field collection). Weather data were also separately extracted for the week preceding each of the two 2017 collection dates (3<sup>rd</sup> to 9<sup>th</sup> August and 29<sup>th</sup> August to 4<sup>th</sup> September 2017). Specifically, daily average temperatures (°C), daily average dew point (°C), maximum daily wind speed (km h<sup>-1</sup>), daily sea level pressure (hPa) and day length (min; sunrise to sunset) were recorded. Precipitation data were downloaded via the UK Met Office Hadley Centre Observation Data (UK Met Office, 2020) as regional precipitation (mm) for South West England & Wales. Weather data were converted to mean values for seven days preceding the collection of spider samples to correspond with the longevity of DNA in the guts of spiders (Greenstone et al., 2014).</p> <p> </p> <p><em>Statistical Analysis</em></p> <p>All analyses were conducted in R v4.0.3 (R Core Team, 2020). To assess how weather affects spider trophic interactions over time, we analysed dietary changes across weather gradients using multivariate models. To identify whether this was likely to be driven by changes in prey abundance, we assessed the corresponding changes in the prey communities and then used null models to ascertain whether spiders were responding to prey abundance changes through prey choice. Given the dependence of linyphiid spiders on webs for foraging, we also compared web height and area over weather gradients to assess whether this may be a component of adaptive foraging. To assess the inter-annual consistency of prey choices in response to weather conditions, we also assessed whether prey preference data could be used to improve the predictive power of prey choice models. For this, we generated null models for 2017 data with prey abundance weighted by prey preferences estimated with the 2018 data. This allowed us to assess the consistency of prey choice under similar conditions, but also provides insight as to whether this framework can be used to predict predator responses to diverse prey communities under dynamic conditions. We detail the specific stages of this analytical framework in the below sections.</p> <p> </p> <p><em>Sampling completeness and diversity assessment</em></p> <p>To assess the diversity represented by the dietary analysis and the invertebrate community sampling, and the completeness of those datasets, coverage-based rarefaction and extrapolation were carried out, and Hill diversity calculated (Chao et al., 2014; Roswell, Dushoff, & Winfree, 2021). This was performed using the ‘iNEXT’ package with species represented by frequency-of-occurrence across samples (Chao et al., 2014; Hsieh et al., 2016; Figures S4 & S6).</p> <p> </p> <p><em>Relationships between weather, spider trophic interactions and prey community composition</em></p> <p>Prey species that occurred in only one spider individual were removed before further analyses to prevent outliers skewing the results. Spider trophic interactions were related to temporal and weather variables in multivariate generalized linear models (MGLMs) with a binomial error family (Wang, Naumann, Wright, & Warton, 2012). Trophic interactions were related to temporal variables and their pairwise interactions (including spider genus to account for any confounding effect), weather variables and their pairwise interactions, and weather variables and their interactions with spider genus and time (to account for any confounding effects) in three separate MGLMs. These variables were separated into different models (Temporal model, Weather interaction model and Confounding effects model) to improve model fit and reduce singularity. Invertebrate communities from suction sampling were related to temporal and weather variables in identically structured MGLMs (excluding the spider genus variable) with a Poisson error family.</p> <p>All MGLMs were fitted using the ‘manyglm’ function in the ‘mvabund’ package (Wang et al., 2012). ‘Temporal model’ independent variables were calendar day (<em>day</em>), mean day length in minutes for the preceding week (<em>day length</em>), spider genus (for dietary models only, to ascertain any effect of spider taxonomic differences on dietary differences over time and day lengths) and all two-way interactions between these variables. ‘Weather interaction model’ independent variables were mean temperature, precipitation, dewpoint, wind speed and pressure for the preceding week, and pairwise interactions between weather variables. ‘Confounding effects model’ independent variables were day (to investigate the interaction between time and weather), spider genus (for dietary models only, to ascertain any effect of spider taxonomic differences on dietary differences over time and day lengths), mean temperature, precipitation, dewpoint, wind speed and pressure for the preceding week, and two-way interactions of each weather variable with day and genus.</p> <p>Trophic interaction and community differences were visualised by non-metric multidimensional scaling (NMDS) using the ‘metaMDS’ function in the ‘vegan’ package (Oksanen et al., 2016) in two dimensions and 999 simulations, with Jaccard distance for spider diets and Bray-Curtis distance for invertebrate communities. For the dietary NMDS, outliers (n = 21; samples containing rare taxa) obscured variation on one axis and were thus removed to facilitate separation of samples and achieve minimum stress. For visualization of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the ‘ordisurf’ function in the ‘ggplot’ package (Wickham, 2016).</p> <p> </p> <p><em>Relationships between web characteristics and weather variables</em></p> <p>Web area and height were compared against weather and temporal variables using a multivariate linear model (MLM) with the ‘manylm’ command in ‘mvabund’ (Wang et al., 2012). Log-transformed web area and height comprised the multivariate dependent variable, and day, spider genus, temperature, precipitation, dewpoint, wind, pressure and two-way interactions between each of these and day and genus comprised the independent variables.</p> <p> </p> <p><em>Variation in spider prey choice across weather conditions</em></p> <p>To separately represent spiders from different weather conditions in prey choice analyses, sample dates for every spider were clustered based on the mean weather conditions (temperature, precipitation, dewpoint, wind and pressure) of the week before collection (7 days, to align approximately with spider gut DNA half-life; Greenstone et al., 2014). Alongside data from 2018 (n = 24 collection dates), two sampling periods from 2017 were included in the clustering to ascertain similarity of weather conditions for additional inter-annual prey choice analyses described below. The clustering process is described in Supplementary Information 3. Five clusters were generated: High Pressure (HPR), Hot (HOT), Wet Low Dewpoint (WLD), Dry Windy (DWI), Wet Moderate Dewpoint (WMD), and 2017 (2017 sampling periods).</p> <p>Prey preferences of spiders in each of the weather clusters was analysed using network-based null models in the ‘econullnetr’ package (Vaughan et al., 2018) with the ‘generate_null_net’ command. Consumer nodes in this case represented spiders belonging to each of the weather clusters. Econullnetr generates null models based on prey abundance, represented here by suction sample data, to predict how consumers will forage if based on the abundance of resources alone. These null models are then compared against the observed interactions of consumers (i.e., interactions of spiders within each weather cluster with their prey) to ascertain the extent to which resource choice deviated from random (i.e., density dependence). The trophic network was visualised with the associated prey choice effect sizes using ‘igraph’ (Csardi & Nepusz, 2006) with a circular layout, and as a bipartite network using ‘ggnetwork’ (Briatte, 2021; Wickham, 2016). The normalised degree of each weather cluster node was generated using the ‘bipartite’ package (Dormann, Gruber, & Fruend, 2008) and compared against the normalised degree of the same node in the null network to determine whether spiders were more or less generalist than expected by random. Prior to the prey choice analysis, an hemipteran prey identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty.</p> <p> </p> <p><em>Validating and predicting relationships between years</em></p> <p>To test how generalisable the results are and the extent to which weather drives prey preferences, we used a measure of prey preference (observed/expected values; observed interaction frequencies divided by interaction frequencies expected by null models) from the above prey choice analysis to assess whether we could more accurately predict observed trophic interactions under similar weather conditions for data from a linked study at the same location in 2017. These additional data represent a subset of the spider taxa analysed above (<em>Tenuiphantes tenuis</em> and <em>Erigone</em> spp.) collected using the same methods by the same researchers and in the same locality (Cuff, Drake, et al., 2021).</p> <p>The similarity in weather conditions between the 2017 study period and each of the five 2018 weather clusters was determined via NMDS of the weather data in two dimensions with Euclidean distance. Centroid coordinates for each 2018 weather cluster and the 2017 data were extracted and pairwise distances calculated between weather clusters:</p> <p> </p> <p>In order, the most proximate weather clusters to the 2017 weather data were HPR (mean Euclidean distance = 8.845), HOT (9.290), WMD (13.626), DWI (13.817) and WLD (18.682; Figure S3).</p> <p>To facilitate comparison between the two years, observed/expected values from the 2018 prey choice models were extracted separately for each of the weather clusters and scaled between 0.1 and 1. For this, 0.1 was used as a minimum since 0 would result in interactions being excluded altogether in the null models, and one as a maximum given the limits of econullnetr but also because this is a multiplier applied to the prey abundances, so greater values would skew prey abundances beyond realistic proportions. Scaling was achieved by the following equation:</p> <p> </p> <p>Missing values (e.g., prey that were absent in certain weather conditions) were represented as 1 to prevent transformation of their abundances in the null models; this treats prey for which data were absent naively, but could increase perceived preferences for them. The scaled values were used to weight the abundance of prey available to the spiders in the 2017 data using the weighting option in econullnetr, whereby values less than 1 proportionally reduce the probability of that taxon being predated in the null models. This effectively redistributes the 2017 relative prey abundance data according to the preference effect sizes generated for each of the 2018 weather clusters. If prey preferences are similar between the 2017 spiders and those from the weather cluster being used to weight the model, the composition of simulated diets should more closely resemble observed diets and fewer significant deviations from the null model should be found.</p> <p>Null models were generated as above (<em>Variation in spider prey choice across weather conditions</em>) but based on the prey availability and trophic interactions from 2017 samples. Three types of model were run: i) a conventional model based on observed prey abundances; ii) a model with prey abundances set to be equal across all prey taxa; and iii) observed prey abundances weighted by prey preferences determined for each of the weather clusters in the 2018 prey choice analysis. A separate model was run for each 2018 weather cluster with abundances weighted by the corresponding scaled observed/expected values. The unweighted conventional model was compared against weighted models to ascertain whether the prey preference weightings from 2018 improved the predictive power of the null models. To compare effect sizes between the unweighted and each other null model for each resource taxon, mean standardised effect size (SES) values were calculated from the paired ‘pre-harvest’ and ‘post-harvest’ data from each model, and paired <em>t</em>-tests were carried out with these between the unweighted and each weighted model. The SES values were plotted for each model and joined between taxa to visualise these paired differences using ‘ggplot’ (Wickham, 2016). Null model-predicted trophic interactions were generated via a modified ‘econullnetr’ function (generate_null_net_indiv) which produces outputs at the individual level to generate simulated diets for individual spiders to compare dietary composition between null model predictions and observed data. These models were run with 2300 simulations to represent 50 simulations per individual spider in the 2017 dataset (n = 46). Null diets were associated with sample IDs by aggregating the 50 simulations per sample and retaining a mean incidence of prey (i.e., mean occurrence across all 50 simulations). A visualisation of the per-sample differences in null model and observed data was generated via NMDS. Mean centroid coordinates for the observed 2017 data and the predicted diets of each model were extracted and the Euclidean distance between the observed data centroid and that of each model was calculated (as above for weather conditions).</p> <p> </p> <p><strong>Supplementary Information 1: Field collection and sample processing</strong></p> <p>Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018 (five visits per week of which spiders from 24 collection dates were used). Transects were randomly distributed across the entire field. Along these transects, 64 separate 4 m<sup>2</sup> quadrats, at least 10 m apart, were searched and all observed linyphiids and lycosids were collected. Spiders were placed in 100 % ethanol using an aspirator, regularly changing meshing to limit potential cross-contamination. Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground (mm) and its approximate dimensions were recorded, the latter calculated as approximate web area (mm<sup>2</sup>). Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until DNA extraction.</p> <p>To obtain data on local prey density, ground and crop stems were suction sampled using a ‘G-vac’ for approximately 30 seconds at each 4 m<sup>2</sup> quadrat (n = 64) from which spiders were collected. The collected material was emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab. All invertebrates were identified to family level to match the resolution of the least resolved of the metabarcoding-derived trophic interaction data, and due to difficulties associated with identification to finer taxonomic resolution for many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level, and wasps of the superfamily Ichneumonoidea which were identified no further due to obscurity of wing venation due to damage.</p> <p> </p> <p><strong>Supplementary Information 2: Molecular analysis and bioinformatics</strong></p> <p><em>Extraction and high-throughput sequencing of spider gut DNA</em></p> <p>Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera <em>Erigone</em>, <em>Tenuiphantes</em>, <em>Bathyphantes</em> and <em>Microlinyphia </em>(Araneae: Linyphiidae), and the Lycosidae genus <em>Pardosa</em>. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key (Roberts, 1993). Abdomens were removed from spiders and again transferred to and washed in fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens (Krehenwinkel et al., 2017).</p> <p>For amplification of DNA, two primer pairs were used. BerenF-LuthienR (Cuff et al., 2021) amplified a broad range of invertebrates including spiders, and TelperionF-LaureR (Cuff et al., 2022), amplified a range of invertebrates but fewer spiders. Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively.</p> <p>Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet <em>et al. </em>(2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads).</p> <p><em>Bioinformatic analysis</em></p> <p>Bioinformatic analysis followed Drake et al., (2022). The Illumina run generated 11,165,405 and 10,959,010 reads for BerenF-LuthienR and TelperionF-LaureR, respectively, which were quality-checked and paired via FastP (Chen et al., 2018) to retain only sequences of at least 200 bp with a quality threshold of 33, resulting in 10,561,874 and 9,355,112 paired reads. The paired reads were demultiplexed and assigned to their respective spider sample according to their MID-tags via the “trim.seqs” command in Mothur v1.39.5 (Schloss et al., 2009), leaving 7,854,610 and 7,437,929 reads with exact matches to the primer and MID-tags.</p> <p>Replicates were removed, and denoising and clustering to zero-radius operational taxonomic units (ZOTUs; clustered without % identity to avoid multiple species represented within a single operational taxonomic unit (OTU)) completed via Unoise3 in Usearch11 (Edgar, 2010). The resultant sequences were assigned a taxonomic identity from GenBank via BLASTn v2.7.1 (Camacho et al., 2009) using a 97 % identity threshold (Alberdi et al., 2017). The BLAST output was analysed in MEGAN v6.15.2 (Huson et al., 2016). Where the top BLAST hit, determined by lowest e-value, was resolved at a higher taxonomic level than species-level, the results were checked; where possibly erroneous entries were preventing species-level assignment (e.g., poorly resolved identifications on GenBank), finer resolution was assigned based on the next-closest match. Where ZOTUs were assigned the same taxon, these were aggregated.</p> <p>Data clean-up used the optimal minimum sequence copy thresholds identified by Drake et al. (2022). The maximum value for a ZOTU present in blank or negative controls was identified and subtracted from all read counts for that ZOTU to remove background contaminants. Simultaneously, known lab contaminants (e.g., German cockroach <em>Blattella germanica</em>), artefacts and errors of the sequencing process, unexpected reads in positive controls and positive control taxon reads in dietary samples were identified. These were calculated as a percentage of their respective sample’s read count and any read counts lower than the highest of these percentages for their respective sample were removed to eliminate additional instances of contamination. These thresholds were defined as 0.38 % and 0.39 % for BerenF-LuthienR and TelperionF-LaureR, respectively. The data from the two libraries (i.e., from each primer pair) were then aggregated together by sample and aggregated again by taxon. Non-target taxa (e.g., fungi) and instances in which predator DNA was amplified (i.e., ZOTUs with high read counts matching the individual’s morphological identity) were removed. All remaining read counts were converted to presence-absence.</p> <p> </p> <p><strong>Supplementary Information 3: Cluster analysis</strong></p> <p>Prior to clustering, weather variables were scaled by subtracting the mean and dividing by the standard deviation. A Euclidean distance matrix was calculated using the ‘dist’ function, and this scaled distance matrix was hierarchically clustered using the ‘hclust’ function. Optimal clustering solutions were determined by comparison of Dunn’s index between methods and <em>k</em> values; this was calculated using the ‘dunn’ function in the “clValid” package (Brock et al., 2008) for each cluster <em>k</em> value above five until the Dunn index decreased. The <em>k</em> value after which Dunn’s index decreased was deemed the optimal solution for each clustering method. Clustering methods based on ‘average’, ‘complete’, ‘single’, ‘median’, ‘centroid’ and ‘mcquitty’ linkages were compared, and the ‘complete’ method selected for subsequent analysis as it resulted in the smallest number of clusters (6; thus, the most efficient simplification of the data; Figure S1). Different clustering methods altered the composition of some clusters, but most sampling dates showed consistent clustering between methods.</p> <p>A heatmap dendrogram was produced using the ‘heatmap.2’ function in the ‘gplots’ package (Warnes et al., 2020), with cluster colours assigned with the ‘Accent’ palette of ‘RColorBrewer’ (Neuwirth, 2014) and relative weather value colour scaling generated using the ‘viridis’ package (Garnier 2018; Figure S2). Weather clusters were named according to unique characteristics relative to the other clusters. These names comprise: High Pressure (HPR; days 142, 253, 256, 250, 173), Hot (HOT; days 162, 204, 205, 208, 201, 187, 198, 197, 183, 184, 194, 190 and 191), Wet Low Dewpoint (WLD; day 121), Dry Windy (DWI; days 131, 169 and 170), Wet Moderate Dewpoint (WMD; days 149 and 152), and 2017 (pre- and post-harvest 2017 sampling periods).</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Alberdi, A., Aizpurua, O., Gilbert, M. T. P., & Bohmann, K. (2017). Scrutinizing key steps for reliable metabarcoding of environmental samples. <em>Methods in Ecology and Evolution</em>, <em>9</em>(1), 1–14. https://doi.org/10.1111/2041-210X.12849</p> <p>Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). clValid: an R package for cluster validation. <em>Journal of Statistical Software</em>, <em>25</em>(4), 1–22.</p> <p>Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: architecture and applications. <em>BMC Bioinformatics</em>, <em>10</em>, 1–9. https://doi.org/10.1186/1471-2105-10-421</p> <p>Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). Fastp: An ultra-fast all-in-one FASTQ preprocessor. <em>Bioinformatics</em>, <em>34</em>(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560</p> <p>Cuff, J. P., Drake, L. E., Tercel, M. P. T. G., Stockdale, J. E., Orozco-terWengel, P., Bell, J. R., Vaughan, I. P., Müller, C. T., & Symondson, W. O. C. (2021). Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. <em>Ecological Entomology</em>, <em>46</em>(2), 249–261.</p> <p>Cuff, J. P., Tercel, M. P. T. G., Drake, L. E., Vaughan, I. P., Bell, J. R., Orozco-terWengel, P., Müller, C. T., & Symondson, W. O. C. (2022). Density-independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops. <em>Environmental DNA</em>, <em>4</em>(3), 549–564.</p> <p>Drake, L. E., Cuff, J. P., Young, R. E., Marchbank, A., Chadwick, E. A., & Symondson, W. O. C. (2022). An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. <em>Methods in Ecology and Evolution</em>, <em>13</em>(3), 694–710.</p> <p>Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. <em>Bioinformatics</em>, <em>26</em>(19), 2460–2461. https://doi.org/10.1093/bioinformatics/btq461</p> <p>Garnier, S. (2018). <em>viridis: default color maps from ‘matplotlib’</em> (0.5.1). https://cran.r-project.org/package=viridis</p> <p>Huson, D. H., Beier, S., Flade, I., Górska, A., El-Hadidi, M., Mitra, S., Ruscheweyh, H. J., & Tappu, R. (2016). MEGAN Community Edition - interactive exploration and analysis of large-scale microbiome sequencing data. <em>PLoS Computational Biology</em>, <em>12</em>(6), 1–12. https://doi.org/10.1371/journal.pcbi.1004957</p> <p>Krehenwinkel, H., Kennedy, S., Pekár, S., & Gillespie, R. G. (2017). A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. <em>Methods in Ecology and Evolution</em>, <em>8</em>, 126–134. https://doi.org/10.1111/2041-210X.12647</p> <p>Neuwirth, E. (2014). <em>RColorBrewer: ColorBrewer palettes</em> (1.1-2). https://cran.r-project.org/package=RColorBrewer</p> <p>Roberts, M. J. (1993). <em>The Spiders of Great Britain and Ireland (Compact Edition)</em> (3rd ed.). Harley Books.</p> <p>Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., Sahl, J. W., Stres, B., Thallinger, G. G., Van Horn, D. J., & Weber, C. F. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. <em>Applied and Environmental Microbiology</em>, <em>75</em>(23), 7537–7541. https://doi.org/10.1128/AEM.01541-09</p> <p>Taberlet, P., Bonin, A., Zinger, L., & Coissac, E. (2018). <em>Environmental DNA</em>. Oxford University Press.</p> <p>Warnes, G. R., Bolker, B., Bonebakker, L., Gentleman, R., Huber, W., Liaw, A., Lumley, T., Maechler, M., Magnusson, A., Moeller, S., Schwartz, M., & Venables, B. (2020). <em>gplots: Various R programming tools for plotting data</em> (R package version 3.1.0). https://cran.r-project.org/package=gplots</p>
Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses
<p>This dataset comprises six-hourly Lamb Weather Type (LWT) time series covering the period 1979-2005 for a) historical experiments run with 61 distinct GCMs from CMIP5 and 6 (specified in "get_historical_metadata.py" published at https://doi.org/10.5281/zenodo.4555367) and b) 3 distinct reanalyses (ERA-Interim, JRA-55 and ERA5, the latter extended to 2020). The LWT time series are provided on a 2.5º regular latitude-longitude grid covering the southern hemisphere between 30ºS and 70ºS. The full LWT approach covering 27 classes is applied and the corresponding results for the Northern Hemisphere were stored in a companion dataset at https://doi.org/10.5281/zenodo.4452080. The format of the files is netCDF-4, compressed with the netCDF Kitchen Sink command "ncks -4 -L 1". The Python code used to generate this dataset is available from https://doi.org/10.5281/zenodo.4555367</p> <p> </p> <p><strong>Note</strong></p> <p>The LWT_SH.zip file contains all relevant data. Please ignore the separate netCDF files outside this zip file. These are old files that could not be deleted during the update from version 1 to 2 due to technical issues with Zenodo.</p> <p> </p> <p><strong>Historial</strong></p> <p>Version 2 is a major dataset update featuring the following improvements:</p> <p>1. The attributes from the netCDF source files "psl...nc" obtained from ESGF were copied into the files available here. These attributes are indicated with the prefix "udata...." (for "underlying data").</p> <p>2. All non-standard calenders from the underlying netCDF files from ESGF were converted into standard using the "xarray.Dataset.convert_calendar" function. The original calendar information was stored as additional netCDF attribute.</p> <p>3. The "patch" method from Python's xesmf module was used to regrid the original psl data from the native GCM grid available from ESGF to the regular lat-lon 2.5° grid common to all applied GCMs and reanalyses.</p> <p>contact: Swen Brands, brandssf@ifca.unican.es</p> <p> </p> <p><strong>Principal Research Articles, Software and Complementary Datasets Associated with this Dataset</strong></p> <p>Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and<br> 6 models for regional climate studies in the Northern Hemisphere mid-to-<br> high latitudes. Geoscientific Model Development, 15 (4), 1375–1411.<br> doi: https://doi.org/10.5194/gmd-15-1375-2022</p> <p>Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod-<br> els for regional climate studies in the northern hemisphere [data set]. Zenodo.<br> doi: https://doi.org/10.5281/zenodo.4452080</p> <p>Brands, S. (2022). Common error patterns in the regional atmospheric circulation<br> simulated by the CMIP multi-model ensemble. Geophysical Research Letters,<br> 49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446</p> <p>Brands, Swen, Tatebe, Hiroaki, Danek, Christopher, Fernández, Jesús, Swart, Neil C., Volodin, Evgeny, Kim, YoungHo, Collier, Mark, Bi, Dave, & Tongwen, Wu. (2022). Python code to calculate Lamb circulation types derived from historical CMIP simulations and reanalysis data. In Geoscientific Model Development: Vols. gmd-2020-418 (Version 4). Zenodo. https://doi.org/10.5281/zenodo.6390256</p> <p>Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández,<br> J. (2023). Auxiliary online material to Brands et al. (2023): A global<br> climate model performance atlas for the Southern Hemisphere extratrop-<br> ics based on regional atmospheric circulation patterns. figshare. doi:<br> https://doi.org/10.6084/m9.figshare.22193443.v1</p> <p>Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández,<br> J. (2023b). Southern Hemisphere Lamb Weather Types from historical<br> GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi:<br> https://doi.org/10.5281/zenodo.7612988</p> <p>Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong-<br> wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1).<br> Zenodo. doi: https://doi.org/10.5281/zenodo.7715383</p> <p>Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J.<br> (2023). 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