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44 results for “individual state”
Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals
<p>This repository contains Electrogastrography signals termed Electrogastrograms (<a href="https://en.wikipedia.org/wiki/Electrogastrogram">EGG</a>) recorded with surface Ag/AgCl electrodes placed over stomach and pre-processed in 20 healthy individuals (8 Females and 12 Males). The method for EGG recording and pre-processing together with subjects' data can be found in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>For each subject, EGG was recorded from three locations before (fasting state) and after (postprandial state) a commercial oat meal (274 kcal). Two 20 minutes recordings (files) are obtained for each subject - fasting and postprandial.</p> <p>Naming convention for files: <strong>subjects ID _ type of recording (fasting / postprandial)</strong>.</p> <p>Sample rate was set at 2 Hz and <a href="https://en.wikipedia.org/wiki/Analog-to-digital_converter">A/D card</a> had 16 bits resolution. Gain of the amplifier was set at 1000. Overall, file size is 7200 samples (2400 samples for each channel). All signals were filtered with 3<sup>rd</sup> order band-pass <a href="https://en.wikipedia.org/wiki/Butterworth_filter">Butterworth filter</a> with cut-off frequencies of 0.03 Hz and 0.25 Hz. In order to avoid phase distortion, zero-phase digital filtering was performed in <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> R2013a by <a href="https://www.mathworks.com/help/signal/ref/filtfilt.html">filtfilt()</a> function. <a href="https://www.gnu.org/software/octave/">GNU Octave</a> code for analysis of EGG signals with statistical calculations presented in <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a> is also provided (<a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>).</p> <p>For convenient test download and appropriate preview, we provided all signals in <a href="https://en.wikipedia.org/wiki/Zip_(file_format)">.zip</a> and sample signal for ID1 in <a href="https://en.wikipedia.org/wiki/Text_file">.txt</a> form.</p> <p><strong>Dataset contents</strong></p> <ol> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/EGG-database.zip?versionId=84315b6b-58da-4655-83f4-8f1d43c3b02c">EGG-database.zip</a>, data files, text format</li> <li><a href="https://zenodo.org/record/3878435/files/eggAnalysis.m?download=1">eggAnalysis.m</a>, GNU Octave code</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/README.txt">README.txt</a>, metadata for data files, text format</li> <li><a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_fasting.txt?versionId=47d0bd09-1a87-42f2-a3e5-ef0c4b4a18e2">ID1_fasting.txt</a> and <a href="https://zenodo.org/api/files/7ef74d7b-694f-4047-b9e5-ef530e8b245d/ID1_postprandial.txt?versionId=c8936a32-2896-44d6-bf3d-2ee37887766c">ID1_postprandial.txt</a>, sample data files for subject ID1, text format</li> </ol> <p><strong>Data files contain numerical values with decimal point according to the following structure</strong></p> <ol> <li>column - CH1* (recorded samples from channel 1)</li> <li>column - CH2* (recorded samples from channel 2)</li> <li>column - CH3* (recorded samples from channel 3)</li> </ol> <p>* For exact anatomical locations for EGG channels CH1, CH2, and CH3, please refer to <a href="http://doi.org/10.1515/bmt-2017-0218">Popović et al. 2019</a>.</p> <p>If you find these signals useful for your own research or teaching class, please cite both relevant paper and dataset as:</p> <ol> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2019. Simple gastric motility assessment method with a single-channel electrogastrogram. <em>Biomedical Engineering/Biomedizinische Technik</em>, <em>64</em>(2), pp.177-185, doi: <a href="https://doi.org/10.1515/bmt-2017-0218">10.1515/bmt-2017-0218</a>.</p> </li> <li> <p>Popović, N.B., Miljković, N. and Popović, M.B., 2020. Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals [Data set]. <em>Zenodo</em>, doi: <a href="https://doi.org/10.5281/zenodo.3730617">10.5281/zenodo.3730617</a>.</p> </li> </ol> <p>DISCLAIMER: The GNU Octave code is provided without any guarantee and it is not intended for medical purposes.</p>
State of Wildfires 2024-25: Regional Summaries of Burned Area, Fire Emissions, and Individual Fire Characteristics for National, Administrative and Biogeographical Regions
<p>This dataset supports the State of Wildfires 2024-25 report under review at <em>Earth System Science Data</em> (Kelley et al., <em>under review)</em>. It is an update of the State of Wildfires 2023-24 report (Jones et al. 2024). The dataset provides annual data and final-year anomalies in burned area (BA), fire carbon (C) emissions, and fire properties (e.g. distributional statistics for fire count, size, rate of growth). Annual data relate to the global fire season defined as March-February (e.g., March 2024-February 2025), aligning with an annuall lull in the global fire calendar (see Jones et al., 2024). The complete methodology is described by Kelley et al. (<em>under review</em>).</p> <h3>Citation</h3> <p>Work utilising our regional summaries should <strong>cite both Kelley et al. (under review) AND the primary reference for the variable(s) of interest</strong> as follows:</p> <ul> <li>Giglio et al. (2018) for MODIS MCD64A1 BA.</li> <li>van der Werf et al. (2017) for GFED4.1s fire C emissions.</li> <li>Kaiser er al. (2012) for GFAS fire C emissions.</li> <li>van der Werf et al. (2017) AND Kaiser er al. (2012) for the average of GFED4.1s and GFAS fire C emissions.</li> <li>Andela et al. (2019) for the Global Fire Atlas.</li> <li>Giglio et al. (2016) for the Fire Radiative Power (FRP) observations.</li> <li>Chuvieco et al. (2024) for FireCCIS311 BA.</li> <li>Giglio et al. (2024) for VIIRS VNP64A1 BA.</li> </ul> <h3>Input Data</h3> <p><strong>Burned Area (BA)</strong></p> <ul> <li>BA data from NASA’s MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available from <a href="https://lpdaac.usgs.gov/products/mcd64a1v061/">Giglio et al. (2021)</a>. <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 500m, daily</li> </ul> </li> <li>BA data from ESA's Climate Change Initiative BA product (FireCCIS311) are extended from Lizundia-Loiola et al. (2022) and are available from <a href="Chuvieco,%20E.;%20Pettinari,%20M.L.;%20Lizundia-Loiola,%20J.;%20Khairoun,%20A.;%20Danne,%20O.;%20Boettcher,%20M.;%20Storm,%20T.%20(2024):%20ESA%20Fire%20Climate%20Change%20Initiative%20(Fire_cci):%20Sentinel-3%20SYN%20Burned%20Area%20Grid%20product,%20version%201.1.%20NERC%20EDS%20Centre%20for%20Environmental%20Data%20Analysis,%2029%20February%202024.%20https://catalogue.ceda.ac.uk/uuid/da8e669a74334c82a56e0b470bc4ef04">Chuvieco et al. (2024)</a>. <ul> <li>Period: 2019-February 2025</li> <li>Resolution: 300m, daily</li> </ul> </li> <li>BA data from NASA’s VIIRS BA product (VNP64A1) are available from <a href="https://lpdaac.usgs.gov/products/vnp64a1v002/">Giglio et al. (2024)</a>. <ul> <li>Period: 2012-February 2025 (only the data after 2019 are used for consistency in the comparisons between MCD64A1, FireCCIS311, and VNP64A1).</li> <li>Resolution: 500m, daily</li> </ul> </li> </ul> <p><strong>Fire Carbon (C) Emissions</strong></p> <ul> <li>GFED4.1s fire C emissions data are extended from van der Werf and are available at <a href="https://globalfiredata.org/">https://globalfiredata.org/</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.25 degree, daily</li> </ul> </li> </ul> <ul> <li>GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available from the <a href="https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation">ECMWF Confluence Server</a>. <ul> <li>Period: 2003-February 2025</li> <li>Resolution: 0.1 degree, daily</li> </ul> </li> </ul> <p><strong>Global Fire Atlas (Individual Fire Properties)</strong></p> <ul> <li>Global Fire Atlas data are extended from Andela et al. (2019) and are available from the repository maintained by <a href="https://doi.org/10.5281/zenodo.11400062">Andela and Jones (2025)</a>. <br> <ul> <li>Period: 2002-February 2025</li> <li>Driven by 500m MODIS BA data (collection 6.1)</li> </ul> </li> </ul> <p><strong>Fire Intensities</strong></p> <ul> <li>FRP data are extended from MOD14A1 and MYD14A1 (Giglio et al., 2016) and are available at <a href="https://lpdaac.usgs.gov/products/mod14a1v061/">Giglio and Justice (2021)</a>.<br> <ul> <li>Period: 2002-February 2025</li> <li>Resolution: 1km, daily</li> </ul> </li> </ul> <h3>Regional Analysis</h3> <p>We performed "cookie-cutting" (spatial and temporal masking) of the above input data sets to features in each of the following regional layers (e.g. per country in the "Countries" layer). </p> <p>The statistics derived from cookie-cutting are listed below. Full details in Kelley et al. (2025).</p> <div> <table> <tbody> <tr> <td> <p>Layer</p> </td> <td> <p>Short Form </p> </td> <td> <p>Source</p> </td> </tr> <tr> <td> <p>Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Ecoregions</p> </td> <td> <p>NA</p> </td> <td> <p>Olson et al. (2001)</p> </td> </tr> <tr> <td> <p>Continents</p> </td> <td> <p>NA</p> </td> <td> <p>ArcGIS Hub (2024)</p> </td> </tr> <tr> <td> <p>Continental Biomes</p> </td> <td> <p>NA</p> </td> <td> <p>See above</p> </td> </tr> <tr> <td> <p>Countries</p> </td> <td> <p>NA</p> </td> <td> <p>EU Eurostat (2020)</p> </td> </tr> <tr> <td> <p>UC Davis Global Administrative Areas (GADM) Level 1</p> </td> <td> <p>GADM-L1</p> </td> <td> <p>UC Davis (2022)</p> <br><br></td> </tr> <tr> <td> <p>Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) Reference Regions </p> </td> <td> <p>IPCC AR6 WGI Regions</p> </td> <td> <p>Iturbide et al. (2020)</p> </td> </tr> <tr> <td> <p>Global C Project Regional C Cycle Assessment and Processes (RECCAP2) Reference Regions</p> </td> <td> <p>RECCAP2 Regions</p> </td> <td> <p>Ciais et al. (2022)</p> </td> </tr> <tr> <td> <p>Global Fire Emissions Database (GFED) Basis Regions</p> </td> <td> <p>GFED4.1s Regions</p> </td> <td> <p>van der Werf et al. (2006)</p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3>Regional Statistics and Anomalies</h3> <ul> <li><strong>Burned Area (BA)</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> </ul> </li> </ul> <ul> <li><strong>Carbon Emissions</strong> <ul> <li>Calculated regional totals for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2003).</li> <li>Ranking amongst all recorded fire seasons.</li> <li>Onset, peak, and cessation based on monthly deviations from climatological means.</li> <li>Statistics available for GFAS, GFED, and their mean.</li> </ul> </li> </ul> <ul> <li><strong>Individual Fire Properties</strong> <ul> <li>Based on values of individual fire size and rate of growth ignition from the ignition point vectors of the Global Fire Atlas.</li> <li>Calculated regional count.</li> <li>Calculated regional maxima and 95th percentiles of fire size and rate of growth for each fire season.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul> <ul> <li><strong>Fire Intensity</strong> <ul> <li>Based on active fire observations of FRP, which are pooled within each fire of the Global Fire Atlas.</li> <li>For each fire, the 95th percentile value of all FRP observations is the assigned intensity value (i.e. a "peak fire intensity" omitting any spurious high-end values).</li> <li>Regionally, the peak fire intensity values are averaged across individual fires.</li> <li>Relative and standardized anomalies from historical data (since 2002).</li> <li>Ranked anomalies among all recorded fire seasons.</li> </ul> </li> </ul>
Estimated individual methane emission rates for oil and gas facilities from the continental United States in 2021
<p>File containing 500 separate estimates of 673,940 individual facility-level methane emission rates for oil and gas facilities for the year 2021 in the continental United States. Each column contains one full estimate of the individual facility-level emissions, presented in units of kilograms per hour of methane per facility. The facility categories included in these estimates are production well sites, gathering and boosting compressor stations, transmission and storage compressor stations, processing plants, and flares. This data can be used to recreate the 500 emission distributions presented in Figure 3 in the following manuscript (link: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1402) which is currently under review. This dataset may be updated as the review stages progress</p>
Text-fig. 3. Schematic geological section of the Kristina Mine near Hrádek/N. (state in 1963–1964) – height/length ratio 3:1. Explanations: vertical hatching – lignite seam, seamlet; dotted – coarse-grained sand, pea-gravel; short lines – sandy clay; white – clay; black lines – clay ironstone concretions; black dots – individual fossiliferous horizons designated as (A) plastic clay from the upper part of the main xylitic seam (about 5 m under t of the seam, (B) clay and "Blätterkohle" from the uppermost part of the first seamlet (split off the Main Coal Seam), (C) slightly sandy brown clay under the uppermost part of the Main Coal Seam, (D) base of the sandy clay with large concretions of the clay ironstone above the Main Coal Seam, (E) sandy clay (incl. clay ironstone) supplying most of leaf material with cuticles (F) 1–2 cm thin silty lenticles or thin beds of the sandy clay with xylites and Eomastixia within peagravels and coarse-grained sands, (G) coarse-grained sands with clayish silts with Fagus, Ocotea, Pterocarya, Tectocarya, (H) brown sandy clay underlying the uppermost seamlet, (I) lignite clay, base of the uppermost seamlet (J) Glyptostrobus – "Blätterkohle", base of the uppermost seamlet (according to Holý 1975, modified). in A Review Of The Early Miocene Mastixioid Flora Of The Kristina Mine At Hrádek Nad Nisou In North Bohemia (The Czech Republic)
Text-fig. 3. Schematic geological section of the Kristina Mine near Hrádek/N. (state in 1963–1964) – height/length ratio 3:1. Explanations: vertical hatching – lignite seam, seamlet; dotted – coarse-grained sand, pea-gravel; short lines – sandy clay; white – clay; black lines – clay ironstone concretions; black dots – individual fossiliferous horizons designated as (A) plastic clay from the upper part of the main xylitic seam (about 5 m under t of the seam, (B) clay and "Blätterkohle" from the uppermost part of the first seamlet (split off the Main Coal Seam), (C) slightly sandy brown clay under the uppermost part of the Main Coal Seam, (D) base of the sandy clay with large concretions of the clay ironstone above the Main Coal Seam, (E) sandy clay (incl. clay ironstone) supplying most of leaf material with cuticles (F) 1–2 cm thin silty lenticles or thin beds of the sandy clay with xylites and Eomastixia within peagravels and coarse-grained sands, (G) coarse-grained sands with clayish silts with Fagus, Ocotea, Pterocarya, Tectocarya, (H) brown sandy clay underlying the uppermost seamlet, (I) lignite clay, base of the uppermost seamlet (J) Glyptostrobus – "Blätterkohle", base of the uppermost seamlet (according to Holý 1975, modified).
Stroke data from: Robust dynamic brain coactivation states estimated in individuals
<p><span>A confluence of evidence indicates that brain functional connectivity (FC) is not static but rather dynamic. </span><span>Capturing transient </span><span>network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an </span><span>individualized network-based dynamics analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task.</span> <span>Moreover, we evaluated the extent to which brain states showed hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics. </span><span>W</span><span>e discovered a right-lateralized brain state that occurred more frequently in men than in women, and more frequently in right-handed versus left-handed individuals. Lastly, we demonstrated longitudinal brain state changes in 42 patients with subcortical stroke over 6 months. Taken together, this approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.</span></p>
Data for: Should I breed or should I go? Manipulating individual state during migration influences breeding decisions in a long-lived bird species
<p>Documentation of Carry-Over Effects (COEs), defined as effects resulting from events that occurred in a previous time period, has largely been observational and understanding of specific mechanisms underlying COEs is still lacking. To investigate this, we simulated an environmental perturbation during the spring migration of a long-lived bird species and looked at the subsequent effects on various breeding parameters. We captured female greater snow geese (<em>Anser caerulescens atlanticus</em>) on their spring staging sites and maintained individuals in captivity for up to four days before releasing them. We re-observed females 3000 km North, on their Arctic breeding grounds, to estimate their breeding propensity (i.e., probability of initiating a reproductive event for a given year), and measure their arrival date, laying date, clutch size, and nesting success. Only proxies of breeding propensity were affected by our manipulation, which decreased as the time spent in captivity increased. However, females were able to overcome the effects of captivity in two out of the three years of experimentation with normal or good environmental conditions at the breeding site. When facing the additional challenge of poor environmental conditions, many individuals manipulated during migration apparently curtailed their reproductive effort by skipping breeding. This experiment is the first to show that breeding propensity is an important parameter affected by COEs resulting from stressful events prior to reproduction in long-lived species.</p>
Individual variation in the avian gut microbiota: The influence of host state and environmental heterogeneity
<div class="abstract-group metis-abstract"> <div class="article-section__content en main"> <p>The gut microbiota have important consequences for host biological processes and there is some evidence that they also affect fitness. However, the complex, interactive nature of ecological factors that influence the gut microbiota has scarcely been investigated in natural populations. We sampled the gut microbiota of wild great tits (<em>Parus major</em>) at different life stages allowing us to evaluate how microbiota varied with respect to a diverse range of key ecological factors of two broad types: (1) host state, namely age and sex, and the life history variables, timing of breeding, fecundity and reproductive success; and (2) the environment, including habitat type, the distance of the nest to the woodland edge, and the general nest and woodland site environments. The gut microbiota varied with life history and the environment in many ways that were largely dependent on age. Nestlings were far more sensitive to environmental variation than adults, pointing to a high degree of flexibility at an important time in development. As nestlings developed their microbiota from one to two weeks of life, they retained consistent (i.e., repeatable) among-individual differences. However these apparent individual differences were driven entirely by the effect of sharing the same nest. Our findings point to important early windows during development in which the gut microbiota are most sensitive to a variety of environmental drivers at multiple scales, and suggest reproductive timing, and hence potentially parental quality or food availability, are linked with the microbiota. Identifying and explicating the various ecological sources that shape an individual's gut bacteria is of vital importance for understanding the gut microbiota's role in animal fitness.</p> </div> </div> <div class="pb-dropzone"> </div>
Data for: Should I breed or should I go? Manipulating individual state during migration influences breeding decisions in a long-lived bird species
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Individual variation in the avian gut microbiota: The influence of host state and environmental heterogeneity
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Stroke data from: Robust dynamic brain coactivation states estimated in individuals
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Number of individuals of each species monthly (from October, 2022 to October, 2023) collected at a shrubby Caatinga area, municipality of São José do Piauí, Piauí state, Brazil.
<p>Dataset included in the manuscript "The orchid bee fauna (Hymenoptera, Apidae, Euglossina) from the Capadócia Nordestina, Piauí State, Brazil", submitted to Biota Neotropica. </p>
Minimal dataset for study, Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018-2020
<p>These datasets are the minimal datasets for the study, Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018-2020.</p> <p>Study DOI: https://doi.org/10.21203/rs.3.rs-4752336/v1</p>
Data from: The effect of individual state on the strength of mate choice in females and males
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Data from: Causes and consequences of individual variation: Linking state-dependent life histories to population performance
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Datasets used for the publication: State-dependence explains individual variation in nest defence behaviour in a long-lived bird
<p>The uploaded datasets were used to test if variation in states predicts nest defence behaviour (a 'risky' behaviour) in a long-lived species, the barnacle goose (<em>Branta leucopsis</em>). Repeated measures of nest defence towards a human intruder (flight initiation distance or FID) of females of known age were collected during 15 breeding seasons. Increasing values of FID represent increasing shyness. Adaptive models have predicted that an individual's residual reproductive value or 'asset' is an important state variable underlying variation in such risk-taking behaviour. Hence, we used this data to investigate how nest defence varies as a function of time of the season and individual age, two state variables that can vary between and within individuals and determine asset.<br> <br> </p>
Data from: Wisdom of the caregivers: pooling individual subjective reports to diagnose states of consciousness in brain injured patients, a monocentric prospective study
OBJECTIVES : The clinical distinction between vegetative state/unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) is a key step to elaborate a prognosis and formulate an appropriate medical plan for any patient suffering from disorders of consciousness (DoC). However, this assessment is often challenging and may require specialized expertise. In this study, we hypothesized that pooling subjective reports of the level of consciousness of a given patient across several nursing staff members can be used to clinically detect MCS. SETTING AND PARTICIPANTS : Patients referred for consciousness assessment were prospectively screened. MCS (target condition) was defined according to the best Coma Recovery Scale-Revised score (CRS-R) obtained from expert physicians (reference standard). "DoC-feeling" score was defined as the median of individual subjective reports pooled from multiple staff members during a week of hospitalisation (index test). Individual ratings were collected at the end of each shift using a 100mm visual analog scale, blinded from the reference standard. Diagnostic accuracy was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity and specificity metrics. RESULTS : 692 ratings performed by 83 nursing staff members were collected from 47 patients. Twenty patients were in a UWS and 27 in a MCS. DoC-feeling scores obtained by pooling all individual ratings obtained for a given patient were significantly greater in MCS than in UWS patients (59.2 mm [IQR: 27.3-77.3] vs. 7.2 mm [IQR: 2.4-11.4]; p<0.001) yielding an AUC of 0.92 (95%CI: 0.84-0.99). CONCLUSIONS: DoC-feeling capitalizes on the expertise of nursing staff to evaluate patients' consciousness. Together with the CRS-R as well as with brain imaging, DoC-feeling might improve diagnostic and prognostic accuracy of DoC patients.
Data from: A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture
1. Spatial capture-recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional non-spatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modelling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. 2. To mitigate the computational burden of large-scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state-space (LESS). Based on prior knowledge about a species' home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. 3. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half-normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57 fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore – the wolverine (Gulo gulo) – with an unprecedented resolution and across the species' entire range in Norway (more than 200 000 km2). 4. Our approach helps overcome a major computational obstacle to population and landscape-level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners that are working at scales that are relevant for conservation and management.
Subspecies and Distribution. S. J. fuscicollis Spix, 1823 — W Brazil (states of Acre & Amazonas) and Peru, S of the Rio Solimoes, between the Rio Javari in the W, E through the Rio Jutai Basin to the Rio Jurua (left bank), also in Peru, W of the Rio Yavari as far as the Rio Tapiche, an E tributary of the Rio Ucayali, and extending N from there as far as the Rio Blanco (left bank), where it meets the distribution of Geoffroy's Saddle-back Tamarin, S. nigrifrons (right bank of the Rio Blanco). S. f. auvilapiresi Hershkovitz, 1966 — W Brazil in the Amazonas State (type locality is the mouth of the Lago de Tefé, Rio Solimoes), found along the S of the Rio Solimoes between the rios Jurua and Purus, including the basins of the rios Urucu and Coari, and probably the Rio Tefé; the S limits are not known but possibly in the region of the N bank of the Rio Tapaua, an affluent of the Rio Purus. S. f. eruzlimai Hershkovitz, 1966 — W Brazil, described by Hershkovitz without provenance (based on a single individual "said to be from the upper Rio Purus"); M. G. M. van Roosmalen reported in 2003 that it had been observed by T. van Roosmalen on 21 June 2002, on the W bank of the Rio Purus, opposite the mouth of the Rio Sepatini, and noted that Hershkovitz was correct in supposing that it occurred between the rios Pauini and Tapaua, W of the Rio Purus. S. J. mura Rohe et al, 2009 — C Brazil (Amazonas State), interfluvium of the rios Madeira and Purus, S of the Rio Amazonas, probably S to the Rio Igapo-Acu. S. f. primitivus Hershkovitz, 1977 — W Brazil (Amazonas State), the distribution is believed to extend from the left bank of the Rio Pauini, along the left bank of the upper Rio Purus, N to the Rio Tapaua (right bank), as far W the right bank of the Rio Jurua, and the Rio Tarauaca. in Callitrichiade
Subspecies and Distribution. S. J. fuscicollis Spix, 1823 — W Brazil (states of Acre & Amazonas) and Peru, S of the Rio Solimoes, between the Rio Javari in the W, E through the Rio Jutai Basin to the Rio Jurua (left bank), also in Peru, W of the Rio Yavari as far as the Rio Tapiche, an E tributary of the Rio Ucayali, and extending N from there as far as the Rio Blanco (left bank), where it meets the distribution of Geoffroy's Saddle-back Tamarin, S. nigrifrons (right bank of the Rio Blanco). S. f. auvilapiresi Hershkovitz, 1966 — W Brazil in the Amazonas State (type locality is the mouth of the Lago de Tefé, Rio Solimoes), found along the S of the Rio Solimoes between the rios Jurua and Purus, including the basins of the rios Urucu and Coari, and probably the Rio Tefé; the S limits are not known but possibly in the region of the N bank of the Rio Tapaua, an affluent of the Rio Purus. S. f. eruzlimai Hershkovitz, 1966 — W Brazil, described by Hershkovitz without provenance (based on a single individual "said to be from the upper Rio Purus"); M. G. M. van Roosmalen reported in 2003 that it had been observed by T. van Roosmalen on 21 June 2002, on the W bank of the Rio Purus, opposite the mouth of the Rio Sepatini, and noted that Hershkovitz was correct in supposing that it occurred between the rios Pauini and Tapaua, W of the Rio Purus. S. J. mura Rohe et al, 2009 — C Brazil (Amazonas State), interfluvium of the rios Madeira and Purus, S of the Rio Amazonas, probably S to the Rio Igapo-Acu. S. f. primitivus Hershkovitz, 1977 — W Brazil (Amazonas State), the distribution is believed to extend from the left bank of the Rio Pauini, along the left bank of the upper Rio Purus, N to the Rio Tapaua (right bank), as far W the right bank of the Rio Jurua, and the Rio Tarauaca.
Distribution. Mainly in Guadalupe I, off the W coast of C Baja California (Mexico), recently has expanded SE to San Benito Is near the Baja California coast, and N to Channel Is (San Miguel I) and other S California islands and to Farallon Is off N California, USA. Wandering individuals have reached as far N as Washington State and one individual to SC Alaska. in Otariidae
Distribution. Mainly in Guadalupe I, off the W coast of C Baja California (Mexico), recently has expanded SE to San Benito Is near the Baja California coast, and N to Channel Is (San Miguel I) and other S California islands and to Farallon Is off N California, USA. Wandering individuals have reached as far N as Washington State and one individual to SC Alaska.
Mechanisms of individual variation in large herbivore diets: Roles of spatial heterogeneity and state-dependent foraging
<p>Many populations of consumers consist of relatively specialized individuals that eat only a subset of the foods consumed by the population at large. Although the ecological significance of individual specialization is recognized, it is difficult to document and its underlying mechanisms are poorly understood. Optimal foraging theory provides a useful framework for predicting how individuals might select different diets, positing that animals balance the 'opportunity cost' of stopping to eat an available food item against the cost of searching for something more nutritious; diet composition should be contingent on the distribution of food, and foragers should be more specialized when individuals have high energy reserves to spend searching for high-quality foods. We tested these predicted mechanisms of individual niche differences by quantifying environmental (resource heterogeneity) and organismal (nutritional condition) determinants of diet in a widespread browsing antelope (bushbuck, <em>Tragelaphus sylvaticus</em>) in an African floodplain-savanna ecosystem. We quantified individual diet breadth and composition using DNA metabarcoding of fecal samples collected repeatedly from 15 GPS-collared animals (6–14 samples per individual, median 12). Bushbuck diets were structured by spatial heterogeneity and constrained by individual conditions. We observed significant individual-level partitioning of food plants by bushbuck both within and between two adjacent habitat types (floodplain and woodland). Individuals with home ranges that were closer together and/or had similar vegetation structures (measured using LiDAR) ate more similar diets, supporting the prediction that heterogeneous resource distribution promotes individual differentiation. Individuals in good nutritional condition had significantly narrower diets (fewer plant taxa), searched their home ranges more intensively (intensity-of-use index), and had higher-quality diets (percent digestible protein) than those in poor condition, supporting the prediction that animals with greater endogenous reserves are more specialized because they can invest time in searching for nutritious foods. Our results support predictions from optimal foraging theory about the energetic basis of individual specialization and provide a potentially generalizable framework for understanding how individual niche width is governed by animal behavior and physiology in heterogeneous landscapes.</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)
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.