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163 results for “temporal variability”
Data from: Coastal upwelling drives ecosystem temporal variability from the surface to the abyssal seafloor.
<p><strong>Abstract</strong></p> <p>Long-term biological time series that monitor ecosystems across the ocean’s full water column are extremely rare. As a result, classic paradigms have yet to be tested. One such paradigm is that variations in coastal upwelling drive changes in marine ecosystems throughout the water column. We examine this hypothesis by using data from three multi-decadal time series spanning surface (0 m), midwater (200-1000 m), and benthic (~ 4000 m) habitats in the central California Current Upwelling System. Data include microscopic counts of surface plankton, video quantification of midwater animals, and imaging of benthic seafloor invertebrates. Taxon-specific plankton biomass and midwater and benthic animal densities were separately analyzed with principal component analysis. Within each community, the first mode of variability corresponds to most taxa increasing and decreasing over time, capturing seasonal surface blooms and lower-frequency midwater and benthic variability. When compared to local wind-driven upwelling variability, each community correlates to changes in upwelling damped over distinct timescales. This suggests that periods of high upwelling favor increases in organism biomass or density from the surface ocean through the midwater down to the abyssal seafloor. These connections most likely occur directly via changes in primary production and vertical carbon flux, and to a lesser extent indirectly via other oceanic changes. The timescales over which species respond to upwelling are taxon-specific and are likely linked to the longevity of phytoplankton blooms (surface) and of animal life (midwater and benthos), that dictate how long upwelling-driven changes persist within each community.</p> <p> </p> <p><strong>Data set description</strong></p> <p>This data set includes 3 files, one for each community. The files contain plankton biomass (for the surface community) or animal density (for midwater and benthos communities) as a function of sampling time and taxonomic group. </p> <ul> <li>surface.csv: autotrophic and heterotrophic surface plankton sampled in Monterey Bay by CTD-rosette and analyzed by epifluorescence microscopy and flow cytometry</li> <li>midwater.csv: midwater animals observed by ROV in the Monterey Bay mesopelagic zone from 200-1000m</li> <li>benthos.csv: benthic animals observed by ROV in a ~ 4000 m abyssal seafloor habitat at the base of the Monterey deep-sea fan</li> </ul> <p><strong>Detailed description </strong>(see additional details and references in <a href="https://www.pnas.org/doi/10.1073/pnas.2214567120">Messié et al., 2023</a>):</p> <p><strong>Surface time series:</strong> Plankton biomass was estimated from surface plankton counts collected using ship-based CTD-rosette at station M1 in Monterey Bay (122.022°W, 36.747°N). This station is part of a 3-station time series program operating in Monterey Bay since 1989 at 3-4 week intervals. Epifluorescence microscopy was used to enumerate and size auto- and heterotrophic plankton. Starting in 1998, flow cytometry samples provided more precise numbers for <em>Synechococcus</em> and eukaryotic picoplankton (<em>Prochlorococcus</em> was not included as no information is available prior to 1998). Standard geometric equations (e.g., ellipsoid, sphere, cylinder, pennate diatom shape) were used to calculate biovolumes of individual cells, and biomass of each plankton group was assessed using biovolume-based carbon conversions. For picoplankton an average value per cell was used: 82 fgC cell<sup>-1</sup> for <em>Synechococcus</em> and 530 fgC cell<sup>-1</sup> for eukaryotic picophytoplankton (red fluorescing picoplankton). Diatom biovolumes were converted to biomass using log<sub>10</sub>(Biomass) = 0.76 log<sub>10</sub>(Volume) - 0.29 where Biomass is in gC and Volume is in 𝜇m<sup>3</sup>. The ciliate conversion was Biomass = 0.08 * Volume. For all other plankton we used log<sub>10</sub>(Biomass) = 0.94 log<sub>10</sub>(Volume) - 0.6.</p> <p><strong>Midwater time series: </strong>Quantitative mesopelagic video transects were conducted at a single station in Monterey Bay (Midwater 1, 36°42′N, 122°02′W). The station is located over the axis of the Monterey Submarine Canyon, where the water column is approximately 1600 m deep. Data were collected using remotely operated vehicles (ROVs). Estimates of animal densities using ROV imaging underestimate some groups (notably fishes), but provide a more complete view of life in the ocean than traditional methods such as nets and acoustics, particularly for gelatinous animals. The ROVs conducted horizontal video transects while moving at about 0.5 m s<sup>-1</sup> for 10 min. Data for this paper come from approximately monthly transects made at 100 m intervals between 200 - 1000 m from 1997-2017. These years were chosen because the entire mesopelagic water column was more evenly surveyed than in the years prior. In each transect, the community of animals was annotated by professional annotators using the open-source Video Annotation and Referencing System (VARS) software. Annotators identified organisms in transect video to the lowest taxon possible; in many cases to species. We selected 63 taxonomic groups defined at the highest possible taxonomic resolution; annotations not included represent 31% of the total (84% of which are euphausiids, chaetognaths, and unidentified appendicularians). Calibrated cameras on MBARI ROVs and accurate measurement of ROV speed through water, allow for the calculation of volume for each transect. Animal density was calculated for each taxonomic group and each depth-specific transect as the number of individuals divided by the corresponding transect volume, further averaged over the water column from 200 - 1000 m. Midwater transecting methods and their efficacy are well-documented.</p> <p><strong>Benthic time series: </strong>Two comparable methods were used to assess benthic communities at Station M (34°50′N, 123°00′W). From 1989-2005, the identification to the lowest possible taxon, and quantity of benthic animals were recorded from images taken by a camera-sled towed along a horizontal transect above the sea floor at a speed of approximately 1 m s<sup>-1</sup>, taking a film image every 4-5 seconds (water depth ~ 4,100 m). The developed film was projected by a Beseler model 23C-II enlarger for annotation of identifiable animals in images. From 2006-2018, benthic communities were assessed using ROV video transects recorded from approximately 1.3 m above the sea floor, with a view of approximately 1 m wide, and length typically approximately 1 km. Water depth for these transects was approximately 4,000 m, the lower depth limit of the ROV. Animals visible in the video were identified and annotated using VARS. The 2006 change in sampling method and in time series location and depth was found to have little impact on the megafauna time series. </p>
The human Voice Areas: spatial organisation and inter-individual variability in temporal and extra-temporal cortices
Open the record for dataset details and reuse information.
Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV-Vis satellite products - Dataset
<p>This data set represents a long-term (1996-2017) harmonised record of lower tropospheric ozone (surface - 450 hPa or surface - approximately 6 km) from satellite instruments. These instruments include the Global Ozone Monitoring Experiment (GOME-1, 1996–2002), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2004) and the Ozone Monitoring Instrument (OMI, 2005–2017). These original products were produced by the Rutherford Appleton Laboratory (RAL) Space using the retrieval scheme described by Miles et al., (2015 - doi:10.5194/amt-8-385-2015). Pre-print of accepted manuscript can be found at https://doi.org/10.5194/egusphere-2023-1172.</p>
[Dataset] Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express
<p>This is the derived data, presented in a publication entitled "Spatial and temporal variability of the 365-nm albedo of Venus observed by the camera on board Venus Express" (JGR:Planet, doi: 10.1029/2019JE006271). See the paper for details. See 'Readme.txt' for the file descriptions.</p>
Dataset - Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities
<p><strong>Dataset simulated for the manuscript "Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities" by Angeles-Martinez and Hatzimanikatis.</strong></p>
Spatial and temporal variability of the freezing level in Patagonia's atmosphere
<h3>Short Summary:</h3> <p>This repository houses the Python preprocessing scripts utilized in generating the metadata for García-Lee et al., (2024) dataset. With these files and scripts, you gain access to the algorithm and examples for generating gridded products in netCDF format, specifically featuring the 0°C isotherm field.</p> <h3>Dependencies:</h3> <ul> <li>numpy (tested with 1.24.4 in py3)</li> <li>pandas (tested with 2.0.3 in py3)</li> <li>netCDF4 (tested with 1.6.0 in py3)</li> <li>re (tested with 2.2.1 in py3)</li> <li>glob</li> <li>OS: Tested in Windows.</li> </ul> <h3>Technical Info:</h3> <table> <tbody> <tr> <td> <p>File</p> </td> <td> <p>Type</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/1_H0_Detect.py?download=1">1_H0_Detect.py</a></p> </td> <td> <p>Python script</p> </td> <td> <p>0°C Isotherm Detection Algorithm.</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/2_Daily_Mean_H0.py?download=1">2_H0_Daily_Mean.py</a></p> </td> <td> <p>Python script</p> </td> <td> <p>Calculation of Daily Mean.</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/ISO0_1959_2021_GRID.nc?download=1">ISO0_1959_2021_GRID.rar</a></p> </td> <td> <p>netCDF</p> </td> <td> <p>0°C Isotherm Data at 6-Hour Intervals (1959-2021) in meters above sea level (m a.s.l.).</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/ERA5_PATAGONIA_6H_T_GPH_1959.rar?download=1">ERA5_PATAGONIA_6H_T_GPH_1959.rar</a></p> </td> <td> <p>netCDF</p> </td> <td> <p>Raw ERA5 data example for 1959: Temperature (°K) and Geopotential (m**2 s**-2).</p> </td> </tr> </tbody> </table> <h3>Extra:</h3> <p>The file 'Observations and Charts.pdf' shows averages, standard deviations, bias, and trends of the 0°C isotherm for Puerto Montt, Río Gallegos, Comodoro Rivadavia, and Punta Arenas. These values were estimated using both observations and reanalysis ERA5 data.</p> <h3>Reference:</h3> <p>García-Lee, N., Bravo, C., Gónzalez-Reyes, Á., and Mardones, P.: Spatial and temporal variability of the freezing level in Patagonia's atmosphere, Weather Clim. Dynam., 5, 1137–1151, https://doi.org/10.5194/wcd-5-1137-2024, 2024.</p>
Data_Temporal_and_inter-farm_variability
<p>Data belonging to the manuscript "Temporal and inter-farm variability of economic and environmental farm performance: a resilience perspective on potato producing regions in the Netherlands?"</p>
Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
<p><span>The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for <em>Bombus</em> sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.</span></p>
Spatial and temporal variability of phytoplankton photophysiology in the Atlantic Southern Ocean
<p>The datasets in this repository are part of the manuscript entitled "<strong>Spatial and temporal variability of phytoplankton photophysiology in the Atlantic Southern Ocean</strong>".</p>
Fig. 1 in Spatial, Temporal And Individual Variability In The Autumn Diet Of European Hare (Lepus Europaeus) In Hungary
Fig. 1. Localities of the study areas. Study areas are shown as gray patches, the capital (Budapest) by striped gray area, Lake Balaton and Lake Tisza by black ones. Black lines are Hungarian rivers and
FIGURE 11. A–B in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 11. A–B: Pearson correlations between the 14 variables and the two PCO axes based on P. leo, P. spelaea, and P. fossilis (thus excluding P. atrox, corresponding to Figure 10B).
FIGURE 10. A in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 10. A: Principal coordinate analyses based on 14 log-transformed morphometric variables. The first axis is parallel with an overall increase in cranial length. Across its whole range, P. spelaea differs from P. fossilis, and P. fossilis is clearly distinct from P. leo. However, P. spelaea from the Western Europe alone does not differ from P. fossilis. B: Principal coordinate analyses based on 14 log-transformed morphometric variables, excluding P. atrox. This analysis shows that P. spelaea from the Western Europe and the Eastern Europe–Asia do not fully overlap in morphospace. The first PCO axis in both analyses correlates strongly positively with all variables and thus reflects the size axis. Specimen numbers: P. atrox (North America): 1–Ichetucknee, 2–Natural Trap Cave, 3–9–Rancho la Brea; P. fossilis: 10–Mauer (Germany), 11–Azé I-3 (France); P. intermedia: 12–Romain-la-Roche (France), 13–Vence (France), 14–Edingen (Germany), 15–Niedźwiedzia Cave (Poland), 16–San (Poland); P. spelaea: 17–Bottrop (Germany), 18– Huttenheim (Germany), 19–Perickhöhlen (Germany), 20–Siegsdorf (Germany), 21–Zoolithenhöhle (holotype; Germany), 22–24–Zoolithenhöhle (Germany), 25–Zandobbio (Italy), 26–Brno (Czech R.), 27–Sloup (Vienna specimen; Czech R.), 28–Sloup (OK 130570, Czech R.), 29–Srbsko–Chlum Komín Cave (Czech R.), 30–Výpustek 1 (Czech R.), 31–Výpustek 3 (Czech R.), 32–Igrita (Croatia), 33–Ursilor (Romania), 34–Binagady (Caucasus, Georgia), 35–Desna (Russia), 36–37–Isa River (South Ural, Russia), 38–Kondakovka (Russia), 39–Mokhokho (Russia), 40–Uzhur (Central Siberia), 41–42–Medvedia Cave (Slovakia); P. leo (Africa and India): 43–50.
FIGURE 8 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 8. Bivariate relationships between the cranial length and nine morphological variables. The lines represent slopes of the relationship between the log-transformed cranial length and nine variables fitted by reduced major axis regression separately for P. fossilis, P. spelaea, and P. leo. Axes are logged.
FIGURE 9 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 9. The summary of mean allometric coefficients (slopes of the relationship between the log-transformed cranial length and nine variables fitted by reduced major axis regression) with 95% bootstrapped confidence intervals. They show more positive allometry for mastoid width, canine width, medial nasale length, and lateral nasal length in P. fossilis relative to other three species (although 95% confidence intervals are broad). The postorbital width shows negative allometry in P. leo whereas it shows positive allometry in P. spelaea and P. fossilis. The missing values in P. atrox did not allow to measure its allometric coefficients for lateral nasal length.
FIGURE 6 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 6. Different types of lion heads in Paleolithic art, maybe indicating the cranial profile type 2 (A), probably the cranial profile type 1 (B), and the cranial profile type 0 (C-F). A: a lion head sculpture from Kostenki near Voronezh, Russia (ca. 23 ka BP, redrawn from Efimenko 1958); B: a lion head sculpture from Vogelherd, Germany (ca. 38-33 ka BP, redrawn from Koenigswald and Schmitt 1987); C: a lion head sculpture from Dolní Věstonice, Moravia–Czech Republic (ca. 27-29 ka BP, redrawn from Jelínek 1972); D: a cave lion depicted in La Marche, France (Magdalenian); E: cave lion pair depicted in Chauvet, France (˂ 26 ka BP); F: cave lions depicted in Chauvet, France (˂ 26 ka BP, redrawn from Clottes 2001). © J. Gullár, 2009-2014 (A–C, F) and the archive of M. Sabol (D and E). Author of illustrations A–C and F is J. Gullár (please, cite it as: J. Gullár in Sabol et al., 2022); D and E are from the archive of M. Sabol.
FIGURE 5 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 5. Boxplots showing differences in cranial length and in the POC/IOB ratio. The horizontal dashed lines approximately span the minima and maxima in P. leo from Mazák (2010).
FIGURE 4 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 4. The types of lion crania distinguished based on cranial profile with the assumed life reconstruction. A: cranium with the straight nasofrontal profile (0)–cranium of Panthera spelaea from the Medvedia jaskyňa Cave in the Západné Tatry Mts. in Slovakia (Last Glacial), B: cranium with the intermediate profile between type 0 and type 2 (1)– cranium of Panthera spelaea from the Zoolithenhöhle Cave in Germany (Last Glacial), C: cranium with the concave nasofrontal profile (2)–cranium of Panthera fossilis from Azé in France (Holsteinian). © J. Gullár, 2011-2021. The crania are not scaled. Author of illustrations is J. Gullár (please, cite it as: J. Gullár in Sabol et al., 2022).
FIGURE 1 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 1. Chronology of lion lineages based on the data from the fossil record and molecular and paleogenetic researches, referred in the "State of art" section. The grey color in the Panthera spelaea lineage suggests questionable evolutionary position of these lion forms. American lions probably represent a separate lineage. The Quaternary chronostratigraphy system is created with the TimeScale Creator software, v. 8.0 (2021).
FIGURE 2. The 12 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 2. The 12 linear cranial characters measured on lion crania. 1: greatest cranial length (L, measured as the distance between the prosthion and the acrocranion), 2: palatal length (LP, measured parasagittally as the distance between the prosthion and the staphylion), 3: medial length of nasals (LMN, measured parasagittally as the distance between the naso-frontal suture and the dorsal margin of the external narial opening), 4: lateral length of nasals (LLN, measured as the distance between the naso-frontal suture and the anteriormost tip of the nasal), 5: greatest nasal width (BN, measured at rostral projection of nasals), 6: the snout width (BS, measured at the level of upper canines), 7: interorbital width (IOB, distance between orbits), 8: maximum cranial width across zygomatic arches (BZ), 9: width across postorbital constriction (POC), 10: greatest neurocranial width (BNC, greatest distance between lateral margins of braincase: euryon–euryon), 11: mastoid width (BM, measured across mastoid processes), and 12: greatest diameter of the auditory bullae (LAB). Cranium drawing modified according to Argant (1991).
FIGURE 3 in Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy
FIGURE 3. Profile depths (blue) and angular variables (red) measured on lion crania. Angle A: the cranial profile angle (nasale–frontale angularity), angle B: the angle between narial aperture and nasofrontal profile (premaxillary– nasofrontale angularity), and angle C: the angle between alveolar margin and postorbital process (maxillary–frontale angularity). Cranium drawing modified according to Argant (1991).
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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.