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911 results for “Temporal data”

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zenodo40/100

Data for "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities"

Data for the paper "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities" which is in minor revision in Ecology Letters (manuscript id:ELE-00589-2021.R1). A doi will be provided upon publication. Current citation: Danet, A., Mouchet, M., Bonnaffé, W., Thébault, E., & Fontaine, C. (In revision) Species richness and food-web structure jointly drive total biomass and its temporal stability in fish communities Minor revision in Ecology Letters. The repository constains data describing fish community monitoring across stream sections in metropolitan France over the period 1995-2018 by the French Office of Water and Aquatic Ecosystems (ONEMA) using electrofishing. The repository contains: <ul> <li> description of fishing: fishing_protocol.csv <ul> <li>surface: sampled surface</li> <li>opcod: fishing operation code, a unique identifier for each sampling event</li> <li>station: unique identifier for each site</li> <li>nb_sp, nb_ind: number of species, number of individuals</li> </ul> </li> <li>geographical information: station_basin.csv <ul> <li>X, Y: spatial coordinates of the station, expressed in metres in Lambert93 (epsg:2154)</li> <li>basin: name of the hydrographic basin</li> </ul> </li> <li>environment: environment.csv ( _mean: mean, _med: median, _cv: coefficient of variation) <ul> <li>alt: altitude</li> <li>d_source: distance to source</li> <li>strahler: strahler order</li> <li>BOD: Biological Oxygen Demand</li> <li>temperature: water temperature</li> <li>flow: water flow</li> </ul> </li> <li>community data: community_data.csv <ul> <li>species: three digits code corresponding to a given species (see Table S1, Danet et al. in revision)</li> <li>nind: number of individuals</li> <li>biomass: biomass in gram</li> </ul> </li> <li>Length of each fish individual: fish_length.csv <ul> <li>length: length of the fish in millimeter</li> </ul> </li> <li>Inferred food-web: class_network.rda <ul> <li>data: <ul> <li>class_id: size class of a fish individual</li> </ul> </li> <li>network: these data.frame can be handled by igraph::graph_from_data_frame() <ul> <li>from, to: "to" eats "from"</li> </ul> </li> <li>composition: <ul> <li>sp_class: concatenation of species and class_id columns</li> <li>bm_std: biomass reported to the sampled surface</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Jul 2021View details →

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A relative-motion method for parsing spatio-temporal behaviour of dyads using GPS relocation data

In this paper, we introduce a novel method for classifying and computing the frequencies of movement modes of intra- and interspecific dyads, focusing in particular on distance-mediated approach, retreat, following and side by side movement modes. Besides distance, other factors such as time of day, season, sex, or age can be included in the analysis to assess if they cause frequencies of movement modes to deviate from random. By subdividing the data according to selected factors, our method allows us to identify those responsible for (or correlated with) significant differences in the behaviour of dyadic pairs. We demonstrate and validate our method using both simulated and empirical data. Our simulated data were obtained from a relative-motion, biased random-walk (RM-BRW) model with attraction and repulsion components. Our empirical data were GPS relocation data collected from African elephants in Etosha National Park, Namibia. The simulated data were primarily used to validate our method while the empirical data were used to illustrate the types of behavioural assessment that our methodology reveals. Our method facilitates automated, observer-bias-free analysis of the locomotive interactions of dyads using GPS relocation data, which are becoming increasingly ubiquitous as telemetry and related technologies improve. It should open up a whole new vista of behavioural-interaction type analyses to movement and behavioural ecologists.

opencc-by-4.0Jul 2021View details →

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Data of "Impact of temporal correlations on high risk outbreaks of independent and cooperative SIR dynamics"

The data reported in the paper: Sajjadi et al. (2021) Impact of temporal correlations on high risk outbreaks of independent and cooperative SIR dynamics. PLoS ONE 16(7): e0253563. https://doi.org/10.1371/journal.pone.0253563 Each directory contains the data illustrated in one figure. The data structure and properties are described in .info files within each directory. All the simulations, analyses and illustrations have been conducted via the Epyc package (written in C++ and Python), developed by Sina Sajjadi. Epyc is available under GPLv3, at https://github.com/Sepante/Epyc.

opencc-by-4.0May 2021View details →

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Data for: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data

1. Quantifying tree defoliation by insects over large areas is a major challenge in forest management, but it is essential in ecosystem assessments of disturbance and resistance against herbivory. However, the trajectory from leaf-flush to insect defoliation to refoliation in broadleaf trees is highly variable. Its tracking requires high temporal- and spatial-resolution data, particularly in fragmented forests. 2. In a unique replicated field experiment manipulating gypsy moth Lymantria dispar densities in mixed-oak forests, we examined the utility of publicly accessible satellite-borne radar (Sentinel-1) to track the fine-scale temporal trajectory of defoliation. The ratio of backscatter intensity between two polarizations from radar data of the growing season constituted a canopy development index (CDI) and a normalized CDI (NCDI), which were validated by optical (Sentinel-2) and terrestrial laser scanning (TLS) data as well by intensive caterpillar sampling from canopy fogging. 3. The CDI and NCDI strongly correlated with optical and TLS data (Spearman's ρ=0.79 and 0.84, respectively). The ∆NCDIDefoliation (A-C) significantly explained caterpillar abundance (R2=0.52). The NCDI at critical time-steps and ΔNCDI related to defoliation and refoliation well discriminated between heavily and lightly defoliated forests. 4. We demonstrate that the high spatial and temporal resolution and the cloud independence of Sentinel-1 radar potentially enable spatially unrestricted measurements of the highly dynamic canopy herbivory. This can help monitor insect pests, improve the prediction of outbreaks, and facilitate the monitoring of forest disturbance, one of the high priority Essential Biodiversity Variables, in the near future.

opencc-zeroSep 2021View details →

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Data for: Spatial and temporal genetic stock composition of river herring bycatch in southern New England Atlantic herring and mackerel fisheries

Anadromous river herring (alewife and blueback herring) persist at historically low abundances and are caught as bycatch in commercial fisheries, potentially preventing recovery despite conservation efforts. We used newly established single-nucleotide polymorphism genetic baselines for alewife and blueback herring to define fine-scale reporting groups for each species. We then determined the occurrence of fish from these reporting groups in bycatch samples from a Northwest Atlantic fishery over four years.Within sampled bycatch events, the highest proportions of alewife were from the Block Island (34%) and Long Island Sound (22%) reporting groups, while for blueback herring the highest proportions were from the Mid-Atlantic (47%) and Northern New England (24%) reporting groups. We then quantified stock-specific mortality in a focal geographic area (~3500 km2 including Block Island Sound) of high bycatch incidence and sampling effort, where the most accurate estimates of mortality could be made. During this period, we estimate that bycatch took about 4.6 million alewife and 1.2 million blueback herring, highlighting the need to reduce bycatch mortality for the most depleted river herring stocks.

opencc-zeroNov 2022View details →

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Data and software for: Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency – fraction of time signal can be detected – but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.

opencc-zeroApr 2023View details →

zenodo40/100

Data: Effects of anterior temporal lobe resection on cortical morphology

Data used for analysis for the paper <a href="http://doi.org/10.48550/arXiv.2212.06529">Effects of anterior temporal lobe resection on cortical morphology</a>. Code used for the analysis can be found on github: <a href="https://github.com/cnnp-lab/2023Leiberg_ATLRmorphology">https://github.com/cnnp-lab/2023Leiberg_ATLRmorphology</a>. The folder "not_corrected" contains morphological data for each subject (pre and post surgery for individuals with TLE) and vertex before application of the gam correction, and corresponding meta data. File names indicate metrics (T=average cortical thickness, At=pial surface area, Ae=exposed surface area), hemispheres (lh=left hemisphere, rh=right hemisphere), and onset sides (RTLE=subjects with right onset TLE, LTLE=subjects with left onset TLE). Controls are included in each file, processed without the temporal lobe for rh_RTLE and lh_LTLE. The folder "age_sex_corrected" contains the data for subjects with TLE with age, sex, and scanning protocol effects removed. Both onset sides have been combined (RTLE hemispheres are switched), and the files contain data for both hemispheres pre- and postoperatively.

opencc-by-4.0May 2023View details →

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Figure 5. BEAST chronogram from a data set corresponding with Table 1 in Verifying Australian Nilotanypus Kieffer (Chironomidae) In A Global Perspective: Molecular Phylogenetic And Temporal Analyses, New Species And Emended Generic Diagnoses

Figure 5. BEAST chronogram from a data set corresponding with Table 1. Values at nodes are time to most recent common ancestor (tmrca) with HPD (95% Highest Posterior Density) intervals in parentheses. The time scale is in millions of years before present.

opencc-by-4.0Jun 2022View details →

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Data from: Insect communities under skyglow: diffuse night-time illuminance induces spatio-temporal shifts in movement and predation

We conducted our experiment at the iDiv Ecotron experimental facility, which is an indoor mesocosm facility consisting of independent, experimental chambers called “EcoUnits”. The Ecotron is located in Bad Lauchstädt, Saxony-Anhalt, Germany, at the Experimental Research Station of the Helmholtz Centre for Environmental Research (UFZ, 51.3917° N, 11.8762° E). Multiple environmental conditions in the EcoUnits can be fully controlled (e.g., nutrient supply and irrigation). Each EcoUnit has internal dimensions of 1.46 m × 1.46 m × 1.50 m (L × W × H, aboveground) and 1.24 m × 1.24 m × 0.80 m (L × W × H, belowground) with the soil surface area measuring 1.54 m². To assess the interactive effects of diffuse nighttime illuminance and landscape structure on animal movement patterns, we established a patch-grassland system which consisted of four meadow patches within each of the corners of an EcoUnit, separated by an area of bare ground. The EcoUnits were filled with 1.23 m3 of unsterilised and homogenised soil from the vicinity of the iDiv Ecotron, and plant communities of 16 plant species were sown on February 4th 2020. We allowed for a settlement phase of roughly 5 months before starting our measurements.   Each heading below describes columns in the two datasets that describe the movement activity (RFID sensor detections) and predation (bite marks left on artificial caterpillar prey dummies) by the experimental insect communities.   timestamp: Continuous value indicating the time and date (format: DD/MM/YYYY hh:mm:ss, timezone: UTC+2) at which a tagged individual was detected by an RFID sensor. We defined detections as distinct and only counted them when they (1) occurred on different sensors or when (2) at least 10 seconds had elapsed (without detection on the same sensor) between two consecutive detections on the same sensor. This prevented the repeated detection of resting or dead animals. block_ID: Categorical value indicating whether an observation was made within the first or second temporal experimental block (b1, b2). Each block corresponded to a period of approximately one lunar cycle (i.e., 28 days: experimental block b1: 21.07.2020 - 18.08.2020, experimental block b2: 15.09.2020 - 13.10.2020). rep_ID: Categorical value indicating the replicate prey dummy exposures (r2, r3, r4, r5) that took place within each 28-day temporal experimental block: r2 and r3 took place successively within block b1; r4 and r5 took place successively within block b2. NAs identify periods during each experimental block where prey dummies had not yet been deployed or were in the process of being collected/re-deployed.   unit_ID: Categorical value indicating the identity of each of the 12 EcoUnits at the iDiv Ecotron experimental facility. patch_ID: Categorical value indicating the identify of each of the four meadow patches within each EcoUnit. Patch identities correspond to their coordinates within the EcoUnit. x: Continuous value indicating the X-coordinate position of an RFID sensor or prey dummy within the Ecotron unit with respect to the position of each EcoUnit’s control panel. y: Continuous value indicating the Y-coordinate position of an RFID sensor or prey dummy within the Ecotron unit with respect to the position of each EcoUnit’s control panel.  day_night: Categorical value indicating whether an individual was detected during the day (treatment lights off) or during the night (treatment lights on). Night includes the periods of dawn and dusk where daylight was gradually (i.e. linearly) brightened or dimmed over the course of two hours before sunrise and sunset, respectively. habitat: Categorical value indicating whether a tagged individual was detected within a meadow patch (Patch) or within the area of bare ground (Matrix) which separates individual patches. tag: Categorical value indicating the unique serial number associated with an RFID-tagged individual. This column is used to estimate local densities (sum of unique tag IDs detected). species: Categorical value indicating the scientific name of the species according to the taxonomy of the Global Biodiversity Information Facility (accessed via GBIF.org during 2020). Note that Harpalus rufipes (De Geer) is referred to in the database by its synonym Pseudoophonus rufipes (De Geer). Poecilus includes the species Poecilus cupreus and Poecilus versicolor. , Harpalus affinis, Harpalus latus) are pooled together at the genus level.  bodymass_mg: Continuous value indicating the live body mass of each tagged individual, excluding the added mass of the RFID tag. detection: Integer with a fixed value of 1 representing the detection of a unique RFID tag. This column is used to estimate movement activity (sum of detections). bite_count: Integer value indicating the number of bite marks recorded on an individual prey dummy during a 14-day exposure. Two independent observers scored the prey dummies by identifying and counting the bite marks left by carabid predators. treatment_lux: Continuous value indicating the treatment of diffuse nighttime illuminance in Lux.

opencc-by-4.0Jun 2023View details →

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Data for detailed temporal mapping of global human modification from 1990 to 2017

Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 1995, 2000, 2005, 2010, 2015, and 2017 to evaluate temporal changes and spatial patterns of land use modification of terrestrial lands (excluding Antarctica). These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km^2 resolution), temporally consistent (for 1990-2015, every 5 years), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset that represents ~2017 conditions and has 14 stressors for an even more comprehensive dataset, but the 2017 results should not be used to calculate change with the other datasets (1990-2015). Note that because of repo file size limits, the datasets for the for the HM overall for 1990 and 1995, as well as major stressors for all years, are located <a href="https://drive.google.com/drive/folders/1D1-S_IuPuPrduBSwCiRfY8b4kPjtho2f?usp=drive_link">this</a> Google Drive. This version 1.5 provides the following updates: <ol> <li> Datasets are provided for each of the 6 stressor groups: built-up areas (BU), agricultural/timber harvest (AG), extractive energy and mining (EX), human intrusions (HI), natural system modifications (NS), and transportation & infrastructure (TI), available now at 300 m resolution for each of the time steps in the 1990-2015 time series. </li> <li> It provides the addition datasets for the years 1995 and 2005, calculated using linear interpolation when stressor data do not provide data at the specific year. </li> <li> The ESA 150 m water-mask dataset (<a href="https://www.mdpi.com/2072-4292/9/1/36">Lamarche et al. 2017</a>) was used to provide better and more consistent alignment of datasets at the ocean-land-inland water interfaces. </li> <li> The built-up stressor uses an updated version of the Global Human Settlement Layer (v2022A). </li> <li> Values provided are 32-bit floating point values, with human modification values ranging from 0.0 to 1.0. </li> </ol> For more details on the approach and methods, please see: Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020. Version 1.5 was completed in collaboration with the Center for Biodiversity and Global Change at Yale University and supported by the E.O. Wilson Biodiversity Foundation.

opencc-byJan 2020View details →

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Data and code for: A quantitative model for spatio-temporal dynamics of root gravitropism

This repository contains the experimental data presented in "A quantitative model for spatio-temporal dynamics of root gravitropism" and Python scripts for the presented root model.

opencc-by-4.0Jul 2023View details →

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Code and data: Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems

Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://doi.org/10.32942/osf.io/mpf5x) The full set of results, including those made available as supplementary material, can be reproduced by running five scripts in the R_codes folder following this sequence: <ul> <li>01_Dataprep_stability_metrics.R</li> <li>02_SEM_analyses.R</li> <li>03_Stab_figs.R</li> <li>04_Stab_supp_m.R</li> <li>05_Sensit_analysis.R</li> </ul> and using the data available in the Input_data folder. The original raw data made available include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://doi.org/10.32942/osf.io/mpf5x However, this is a collaborative effort and not all authors are allowed to share their raw data. One data set (LEPAS), out of 30, was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). So, in code "01_Dataprep_stability_metrics.R" all data made available are imported, except the LEPAS data set. For this specific data set, code "01_Dataprep_stability_metrics.R" imports variability and synchrony components estimated using the methods described in Wang et al. (2019 Ecography; doi/10.1111/ecog.04290), diversity metrics (alpha and gamma diversity), and some variables describing the data set. A protocol for requesting access to the LEPAS data sets can be found here: https://ael.osu.edu/researchprojects/lake-erie-plankton-abundance-study-lepas Dataset owner: Ohio Department of Natural Resources – Division of Wildlife, managed by Jim Hood, Dept. of Evolution, Ecology, and Organismal Biology, The Ohio State University. Email: hood.211@osu.edu Anyone who wants to reproduce the results described in the preprint can just download the whole R project (that includes code and data) and run codes from 01 to 05. I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file.

opencc-by-4.0Sep 2023View details →

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