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22 results for “spatiotemporal variability”
Dataset of five years of in-situ and satellite derived chlorophyll a concentrations and its spatiotemporal variability in the Rotorua Lakes, New Zealand
<p><strong>rotorua_chl_fields_2015-2020.nc</strong> is a time series of 283 <em>Chl</em> fields of 13 of the lakes derived from Sentinel-2 MSI images with a regionalised parametrization of the C2RCC algorithm at 60 m pixel resolution. It also includes C2RCC and Idepix masks as well as a shoreline-and-shallow-water-buffer for flexible quality flagging.</p> <p><strong>rotorua_chl_spatial_variability.tif</strong> is a GeoTIFF that illustrates the representativeness of each grid cell for the <em>Chl</em> distribution in each lake and thus indicates recurring spatial patterns. The file contains three bands. Each band shows the relative frequency (in %) which <em>Chl</em> concentration was found near the median, or upper or lower quartile, respectively. The intervals around the median and quartiles are 5% to either side.</p> <p><strong>rotorua_insitu_chl_2015-2019.csv</strong> contains 831 in situ <em>Chl</em> measurements from 12 of the lakes collected between 2015 and 2019. The majority of these measurements (802) have been taken as part of the monthly Bay of Plenty lake water quality monitoring programme, in which 11 lakes are monitored. The data set also contains samples from field work under the <em>Eye on Lakes</em> project (University of Waikato) obtained by one of the authors (MKL). These 29 samples also include two measurements at Lake Rotokakahi, which is not part of the monthly monitoring program.</p> <p><strong>shoreline_shallow_water_buffer.zip</strong> contains a shapefile with polygons of the valid water pixels of all lakes to remove areas contaminated by bottom reflectance in remote sensing products. Each lake has a 120 m shoreline buffer to avoid mixed land-water pixels to reduce adjacency effects. It further excludes lake areas shallower than the 95%-quantile of all Secchi depth measurements of the Bay of Plenty lake water quality monitoring programme.</p>
Processed ERA5, IMERG and TRMM PR/GPM DPR precipitation data for Nicolas & Boos - "Understanding the spatiotemporal variability of tropical orographic rainfall using convective plume buoyancy."
<p>The dataset contains processed data from large datasets that are freely available online. <br>All data cover the period 01/2001 - 12/2020. The file names describe the months & region that each file contains. Variable codes for ERA5 data (all files starting in e5.) are:</p><p> - 228_246_100u : 100m u-wind<br> - 228_247_100v : 100m v-wind<br> - qL : 900-600hPa averaged specific humidity<br> - thetaeb : surface - 900hPa averaged equivalent potential temperature<br> - thetaeL : 900-600hPa averaged equivalent potential temperature<br> - thetaeLstar : 900-600hPa averaged saturation equivalent potential temperature<br> - tL : 900-600hPa averaged temperature<br> - uBL : surface - 900hPa averaged u wind<br> - vBL : surface - 900hPa averaged v wind<br> - 128_034_sstk : sea surface temperature<br> - 162_071_viwve : eastward component of vertically integrated water vapor transport<br> - 162_072_viwvn : northward component of vertically integrated water vapor transport</p><p> </p>
Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions
<p>Understanding geographic ranges and species distributions is crucial for effective conservation, especially in the light of climate and land use change. However, the spatial, temporal and intraspecific resolution of digital accessible information on species distributions is often limited. Here, we suggest to make better use of high-resolution tracking data to address existing limitations of occurrence records such as spatial biases (e.g. lack of observations in parts of the geographic range), temporal biases (e.g. lack of observations during a certain period of the year), and insufficient information on intraspecific variability (e.g. lack of population- or individual-level variation). Addressing these gaps can improve our knowledge on geographic ranges, intra-annual changes in species distributions, and population-level differences in habitat and space use. We demonstrate this with tracking data and species distribution models (SDMs) of the Barnacle Goose, a migratory bird species wintering in western Europe and breeding in the Arctic. Our analyses show that tracking data can (1) supplement occurrence records from the Global Biodiversity Information Facility (GBIF) in remote areas such as the European and Russian Arctic, (2) improve information on the temporal use of wintering, staging and breeding areas of migratory species, and (3) provide insights into the differences of population-level responses to environmental variables. We recommend a broader use of tracking data to address the Wallacean shortfall (i.e. the incomplete knowledge on the geographic distribution of species) and to improve forecasts of biodiversity responses to climate and land use change (e.g. species vulnerability assessments). To avoid common pitfalls, we provide six recommendations for consideration during the research cycle when using tracking data in species distribution modelling, including steps to assess biases and integrate information on intraspecific variability in modelling approaches.</p>
Data and Code: Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway
<h3>Code</h3> <p><strong>MATLABWhaleDetectionCodeNWA.zip</strong></p> <p>The code used in the manuscript Spatiotemporal Variability of Fin Whale and Blue Whale Calls Detected by Land Seismometers in the Lower St. Lawrence Seaway<em> </em>(Goblot et al., in review)<em> </em>to detect whale calls in seismic waveforms was written by Alexandre Plourde and uploaded here with permission. This .zip file contains 3 folders, ‘Numerical Methods’, ‘Signal Processing’ and ‘Whale Subroutines’, as well as 3 main scripts (x2 for fin and blue whales). </p> <ul> <li>Pre-processing: <ul> <li>Convert .mseed to SAC files and rename with the format: YYYY.MM.DD.NETWORK.STATION..CHANNEL.SAC. Each SAC file must then be placed in a folder named with the format YYYYMMDD, referred to as events (evs) in the code. Each of these folders must then be placed into a single folder known as the events directory (drE) in the code.</li> </ul> </li> </ul> <ul> <li>Processing: Fin whales <ul> <li>Run ‘RecordWhaleNoiseLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘FinWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createFinWhaleDectectionList.m' to check which 120s time windows have W > threshold (3.0 in our case). These time windows are stored in the matrix FWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 120s time segments in the FWD matrix. The labelled call times are stored in the matrix FWC.</li> </ul> </li> </ul> <ul> <li>Processing: Blue whales <ul> <li>Run ‘RecordBlueWhalesLSZ.m’ for fin whales. This reads through daily SAC files, and computes fin whale power ratio every 120 s. The folder ‘BlueWhalePower’ (drP) should now be created. These contain text files with the power ratios (W) for every window at each station.</li> <li>Run ‘createBlueWhaleDectectionList.m' to check which 720s time windows have W > threshold (1.5 in our case). These time windows are stored in the matrix BWD.</li> <li>Run 'RecordFinWhaleCallsLSZ.m' to identify individual whale calls within each of the 720s time segments in the BWD matrix. The labelled call times are stored in the matrix BWC.</li> </ul> </li> </ul> <h3>Data</h3> <p>The whale detection code was applied to seismic waveform data downloaded through the EarthScope Consortium Web Services (<a href="https://service.iris.edu/" target="_blank" rel="noopener">https://service.iris.edu/</a>), including the following seismic network: CN (Natural Resources Canada, 1975). All whale calls were detected using the characteristic reccurence interval method (MATLABWhaleDetectionCodeNWA.zip).</p> <p><strong>Table S2</strong></p> <p><strong>WhaleDetectionsFeb2020Jan2022LSZ.mat</strong></p> <ul> <li>This dataset contains the center time of fin whale and blue whale detections and calls, from land 6 seismometers (CNQ, ICQ, SMQ, SNFQ, PMAQ, RISQ) in the Lower St-Lawrence Seaway between February 2020 and January 2022. <ul> <li>FWD_ <ul> <li>Column 1: list of fin whale detections (2 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWD_ <ul> <li>Column 1: list of blue whale detections (12 minute time window with presence)</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>FWC_ <ul> <li>Column 1: list of individual ~1s fin whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>BWC_ <ul> <li>Column 1: list of individual ~8s blue whale calls</li> <li>Column 2: corresponding station label</li> </ul> </li> <li>stadir: list of stations and labels (1-6)</li> </ul> </li> </ul> <p><strong>Table S3</strong></p> <p>a)<strong> MonthlyBlueWhaleDetectionsOct2015Jan2022.csv</strong> and b) <strong>MonthlyFinWhaleDetectionsOct2015Jan2022.csv</strong></p> <ul> <li> <div> <div> <div> <p>Number of monthly a) fin whale detections and b) blue whale detections from October 2015 to January 2022. Empty cells indicate periods when stations were not operating. Quiet day detections are included.</p> <p>Note the Oct 2015-Feb 2020 catalogue is from Plourde and Nedimović (2022) and includes up to 14 stations throughout this period from the following seismic networks: CN (Natural Resources Canada, 1975) and C8 (Natural Resources Canada, 2002). The Feb 2020-Jan 2022 catalogue is from Goblot et al. (in review) and includes the same stations from Table S1.</p> </div> </div> </div> </li> </ul> <h3>Additional</h3> <p><strong>Movie S1</strong></p> <p><strong>LSLSWhales.mp4</strong></p> <ul> <li>This movie contains an audiovisual representation of a series of fin whale calls and blue whale calls detected by 2 land seismometers in the Lower St-Lawrence Seaway. The fin whale detection was recorded by land seismometer CNQ (Côte-Nord) on Dec 12 2021 from 18:19:40 to 18:21:40. The blue whale detection was recorded by land seismometer SNFQ (Sainte-Félicité) on Aug 22 2021 from 05:36:00 to 05:48:00.</li> <li>The MATLAB soundsc(x) function was applied to seismic waveform data with fin whale and a blue whale calls. The signal with the fin whale detection is bandpassed from 18-21 Hz and the blue whale signal is bandpassed from 16-18 Hz. These signals were sped up 1000x in order to make them audible.</li> <li>The audio file was then uploaded to veed.io to produce a frequency response visualization of the whale calls.</li> <li>More audiovisuals can be viewed <a href="https://seismicsoundscapes.myportfolio.com/" target="_blank" rel="noopener">here</a>.</li> </ul>
Figure 3. CCA showing the relationship between 16 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 3. CCA showing the relationship between 16 species (red triangles) and 9 environmental variables (red arrows). See Tables 2 and 4 for an explanation of abbreviations and variables.
Figure 2 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 2. Jaccard's coefficient similarity dendrograms showing the faunal similarity among the 12 sampling sites (based on presence/absence of species) and clustering relationships among the 16 ostracod species. (Species codes are given in Table 3.)
Fig. 1 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Fig. 1. Map of the eight studied coastal lagoons. Selected sampling sites at Tuzla Lake 1 (St-1), Tuzla Lake 2 (St-2), Tuzla Lake 3 (St-3), Taz (St-4), Işık (St-5), Dalyan (St-7, 8, and 9), Kuvalak (St-10), and Taşaltı (St-11 and 12) were used for comparisons of the lagoons. The sampling sites are indicated by red circles; the red arrows show the direction of water currents.
Spatiotemporal variability of stable isotopes in precipitation and stream water in a high elevation tropical catchment in the Central Andes of Colombia
<p>Stable isotopes data set for the manuscript "Spatio-temporal variability of stable isotopes in precipitation and stream water of a high elevation tropical catchment in the Central Andes of Colombia".</p> <p>Data also used by Andrés Tangarife-Escobar for the thesis "Analysis of the spatial and temporal distribution of stable isotopes and their driving factors in the Upper Claro River Basin, Colombian Andes" to obtain the title of MSc in "Tropical Hydrogeology and Environmental Engineering" at the Technische Universität Darmstadt (Germany) in 2019. </p> <p>Samples collected by Jorge Ceballos from IDEAM (Colombia) and analyzed by the Servicio Geologico Colombiano. </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: Making better use of tracking data can reveal the spatiotemporal and intraspecific variability of species distributions
Open the record for dataset details and reuse information.
Migration variables and infection data for article "Effects of blood parasite infections on spatiotemporal migration patterns and activity budgets in a long-distance migratory passerine"
<p>Data related to the article "Effects of blood parasite infections on spatiotemporal migration patterns and activity budgets in a long-distance migratory passerine" in the format .csv. The 1st file (SupplTab_EcoEvo_data.csv) contains all variables used for the analyses and the 2nd file (SupplTab_EcoEvo_glossary.csv) contains explanations about the variables in the 1st file.</p>
Drivers of spatiotemporal variability in bycatch of a top marine predator: First evidence for the role of water turbidity in protected species bycatch
1. Bycatch of protected species in static net fisheries is a global conservation concern and is currently considered the dominant anthropogenic threat to many marine mammal species worldwide. Effective bycatch mitigation remains challenging, contingent on an understanding of the underlying mechanisms that cause individuals to become entangled. 2. We combined data collected by scientific observers and fishers to identify predictors of seal bycatch in static net fisheries along the west, southwest, and south coasts of Ireland. We first analysed the broad regional and seasonal trends in seal bycatch before identifying environmental variables that could potentially explain these patterns. 3. Based on negative binomial generalised linear mixed effects models, the rate of seal bycatch significantly varied with season and region, and decreased at greater distances to major seal colonies and lower water turbidity. 4. Synthesis and applications. Our results suggest that distance to major seal colonies was a significant driver of the observed regional differences in seal bycatch rates, and water turbidity a major driver of seasonal trends. These findings will enable us to identify future bycatch risk and target mitigation measures accordingly. This is the first study to identify the effect of water turbidity on bycatch of a protected marine species. Increasing net visibility in turbid waters may provide a novel approach to mitigating against protected species bycatch in static net fisheries.05-Nov-2019
Figure 4 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 4. Salinity tolerance diagram of Ostracoda determined in the Enez lagoons.
Supporting data to Heterogeneity in spatiotemporal variability of High Mountain Asia's runoff and its underlying mechanisms
<p>This dataset includes (1) the boundary of 12 basins over the southern High Mountain Asia, (2) their climate and catchment properties, (3) significance of interannual, interdecadal and multidecadal variability of runoff and corresponding climate variables, and (4) the decomposed timeseries of runoff and its related atmospheric drivers. Please see the publication for more details.</p>
Supporting data to Heterogeneity in spatiotemporal variability of High Mountain Asia's runoff and its underlying mechanisms
<p>This dataset includes (1) the boundary of 12 basins over the southern High Mountain Asia, (2) their climate and catchment properties, (3) significance of interannual, interdecadal and multidecadal variability of runoff and corresponding climate variables, and (4) the decomposed timeseries of runoff and its related atmospheric drivers. Please see the publication for more details.</p>
Drivers of spatiotemporal variability in bycatch of a top marine predator: First evidence for the role of water turbidity in protected species bycatch
Open the record for dataset details and reuse information.
Database for "Accurate estimates of past spatiotemporal temperature variability could strongly constrain future warming"
<p>This repository contains data used to support findings of the study "Accurate estimates of past spatiotemporal temperature variability could strongly constrain future warming", which is currently under review.</p>
Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems
<div> <p><span>Coastal </span><span>methane (CH<sub>4</sub>) emissions dominate the global ocean CH<sub>4</sub> budget and can offset the "blue carbon" storage capacity of vegetated coastal ecosystems. However, current estimates lack systematic, high-resolution, and long-term data from these intrinsically heterogeneous environments, making coastal budgets </span><span>sensitive to statistical assumptions and uncertainties</span><span>. Using continuous CH<sub>4</sub> concentrations, δ<sup>13</sup>C-CH<sub>4</sub> values, and CH<sub>4</sub> sea-air fluxes across four seasons in three globally pervasive coastal habitats, we show that the CH<sub>4 </sub>distribution is spatially patchy over meter-scales and highly variable in time. Areas with mixed vegetation, macroalgae, and their surrounding sediments exhibited a spatiotemporal variability of surface water CH<sub>4 </sub>concentrations ranging two orders of magnitude (i.e., 6 – 460 nM CH<sub>4</sub>) with habitat-specific seasonal and diurnal patterns. We observed (1) δ<sup>13</sup>C-CH<sub>4</sub> signatures that revealed habitat-specific CH<sub>4</sub> production and consumption pathways, (2) daily peak concentration events that could change >100% within hours across all habitats, and (3) a high thermal sensitivity of the CH<sub>4 </sub>distribution signified by apparent activation energies of </span><span>∼</span><span>1 eV that drove seasonal changes. </span><span>Bootstrapping simulations show that scaling the CH<sub>4</sub> distribution from few samples involves large errors,</span><span> and that </span><span>∼</span><span>50 concentration samples per day are needed to resolve the scale and drivers of the natural variability and improve the certainty of flux calculations by up to 70%. Finally, we identify northern temperate coastal habitats with mixed vegetation and macroalgae as understudied but seasonally relevant atmospheric CH<sub>4</sub> sources (i.e., releasing ≥100 μmol CH<sub>4</sub> m<sup>−2</sup> day<sup>−1</sup> in summer). Due to the large spatial and temporal heterogeneity of coastal environments, high-resolution measurements will improve the reliability of CH<sub>4</sub> estimates and confine the habitat-specific contribution to regional and global CH<sub>4</sub> budgets.</span></p> </div>
Data from: High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems
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Data from: Spatiotemporal variability in the structure of seagrass meadows and associated macrofaunal assemblages in southwest England (UK): using citizen science to benchmark ecological pattern
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Spatiotemporal variability of N-bearing components across China during COVID-19 period
<p>Spatiotemporal variability of N-bearing components across China during COVID-19 period</p>
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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)
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DANDI Archive for NWB datasets
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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.