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9,786 results for “selection”
Data from: Home range and habitat selection of wolves recolonising Central European human-dominated landscapes
<p>Decades of persecution has resulted in the long-term absence of grey wolves (<em>Canis lupus</em>) from most European countries. However, recent changes in both legislation and public attitudes toward wolves has eased the pressure, allowing wolves to rapidly re-establish territories in their previous Central European habitats over the last 20 years. Unfortunately, these habitats are now heavily altered by humans. Understanding the spatial ecology of wolves in such highly modified environments is crucial, given the high potential for conflict and the need to reconcile their return with multiple human concerns. We equipped 20 wolves, originating from seven packs in six Central European regions, with GPS collars, allowing us to calculate monthly average home range sizes for 14 of the animals of 213.3 km2 using Autocorrelated Kernel Density Estimation. We then used ESA WorldCover data to assess the mosaic of available habitats used within each home range. Our data confirmed a general seasonal pattern for breeding individuals, with smaller apparent home ranges during the reproduction phase, and no specific pattern for non-breeders. Predictably, our wolves showed a general preference for remote areas, and especially forests, though some wolves within military training areas also showed a broader preference for grassland, possibly influenced by local land use and high availability of prey. Our results provide a comprehensive insight into the ecology of wolves during their re-colonisation of Central Europe. Though wolves are spreading relatively quickly across Central European landscapes, their permanent reoccupation remains uncertain due to conflicts with the human population. To secure the restoration of European wolf populations, further robust biological data, including data on spatial ecology, will be needed to clearly identify any management implications.</p>
Data and code from: Recreational fisheries selectively capture and harvest large predators
<p>Size and species selective harvest, inevitably alters the composition of targeted populations and communities. This can potentially harm fish stocks, ecosystem functionality, and related services, as evidenced in numerous commercial fisheries. The high popularity of rod-and-reel recreational fishing, practiced by hundreds of millions globally, raises concerns about similar deteriorating effects. Despite its prevalence, the species and size selectivity of recreational fisheries remain largely unquantified due to a lack of combined catch data and fisheries-independent surveys. This study addresses this gap by using standardised monitoring data and over 60,000 digital angling catch reports from 62 distinct fisheries. The findings demonstrate a pronounced selectivity in recreational fisheries, targeting top-predators and large individuals. Catch-and-release practices reduced the overall harvest by 60 % but did not substantially alter this selectivity. The strong species- and size-specific selectivity mirror patterns observed in other fisheries, emphasising the importance of managing the potential adverse effects of recreational fisheries selective mortality and overfishing.</p>
Euclid Early Release Observations of Abell 2390 & 2764: NISP-selected photometry and photometric redshift catalogue
<p><strong>Euclid Early Release Observations 'Magnifying Lens' (PI: Atek)</strong><br><strong>Elementary Photometric Catalogues and Photometric Redshifts</strong></p> <p>Version 1.0.0</p> <p>Authors: J. Weaver (UMass), S. Taamoli (UCR), and H. Atek (IAP)<br>Contact: john.weaver.astro@gmail.com</p> <p>If you use this work, please cite:</p> <p>Survey Paper: Atek et al. 2024<br>Processing Paper: Cuillandre et al. 2024<br>Demonstration Paper: Weaver et al. 2024</p> <p>While photometric redshfits are provided, we caution that they are derived from only four optical-NIR bands and are designed for high-z dropout galaxies. Users may encounter issues at lower redshifts.</p> <p>These files are provided 'as is'. We the authors retain the right to modify the files at any time.</p> <p>Please see the dedicated README file for details.</p>
Data for: Characteristics of Selected Open Infrastructures, 2024 State of Open Infrastructure Report
<p>The State of Open Infrastructure report provides an annual snapshot of general characteristics for open infrastructures (OIs) listed in Invest in Open Infrastructure’s (IOI) open infrastructure selection tool, Infra Finder (https://infrafinder.investinopen.org/).</p> <p>The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “Characteristics of selected open infrastructures.” The full report is available at https://doi.org/10.5281/zenodo.10934089.</p> <p>A readme, data dictionary, and additional metadata definition file are provided with the dataset with additional detail.</p>
Dataset: Location- and feature-based selection histories make independent, qualitatively distinct contributions to urgent visuomotor performance
<p>This dataset (packaged as the zip file history_share.zip) accompanies the article titled "Location- and feature-based selection histories make independent, qualitatively distinct contributions to urgent visuomotor performance" by EE Oor, E Salinas, and TR Stanford which is available as a preprint in bioRxiv. The experimental results in the article are based on behavioral data collected from 2 monkey subjects during performance of a visuomotor task (the compelled oddball task), as described in the text. This dataset contains the trial-by-trial behavioral results collected for each subject and upon which all subsequent analyses were based.</p> <p>In addition to the trial-wise data arrays (stored in the files dataC.csv, dataN.csv, and dataCN.csv), the package includes Matlab functions and scripts (*.m files) used to analyze the data and recreate the results and figures in the article. Instructions and specifics are detailed in the README file. </p>
ERA5-Land selected indicators daily aggregates for Africa, 1991
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1991.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Fig. 5. Selected reptile species found during the 2012–2016 in Endemism on a threatened sky island: new and rare species of herpetofauna from Cerro Chucantí, Eastern Panama
Fig. 5. Selected reptile species found during the 2012–2016 surveys on Cerro Chucantí. (A) Echinosaura aff. palmeri; (B) Ptychoglossus aff. plicatus; (C) Anolis aff. fuscoauratus; (D) Geophis aff. brachycephalus; (E) Corallus annulatus, highest elevation record; (F) Tantilla berguidoi, recently described and endemic (Batista et al. 2016b); (G) Bothrops asper, 1,273 m asl, highest elevation record for Panama; (H) Lachesis acrochorda juvenile, 1,011 m asl, highest elevation for this species in Panama.
Fig. 3. Selected amphibian species found during the 2012–2016 in Endemism on a threatened sky island: new and rare species of herpetofauna from Cerro Chucantí, Eastern Panama
Fig. 3. Selected amphibian species found during the 2012–2016 surveys on Cerro Chucantí. (A) Dermophis aff. glandulosus; (B) second known specimen of Bolitoglossa chucantiensis, recently described and endemic (Batista et al. 2014a); (C) Bolitoglossa aff. biseriata; (D) Oedipina aff. complex; (E) Strabomantis bufoniformis; (F) Diasporus majeensis, recently described and endemic (Batista et al. 2016a); (G) Pristimantis gaigei; (H) Pristimantis moro.
Fig. 4. Selected amphibian species found during the 2012–2016 in Endemism on a threatened sky island: new and rare species of herpetofauna from Cerro Chucantí, Eastern Panama
Fig. 4. Selected amphibian species found during the 2012–2016 surveys on Cerro Chucantí that await formal description or clarification of relationships. (A) Colostethus aff. pratti; (B) Silverstoneia sp.; (C) Pristimantis aff. latidiscus; (D) Pristimantis aff. ridens.
Data from: "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal"
<p>Processed datasets used for analysis in "Landscape context and behavioral clustering contribute to flexible habitat selection strategies in a large mammal" by Hooven et al. published in <em>Mammal Research</em>. R scripts used to process and analyze these data are available from: <a href="https://github.com/nhooven/elk-individual-habitat">https://github.com/nhooven/elk-individual-habitat</a></p> <p>WS_sampled.csv, SU_sampled.csv, UA_sampled.csv, AW_sampled.csv - Processed telemetry datasets (with relocation data removed), resultant files from script "01 - Pre-processing.R".</p> <p>WS_HRs.csv, SU_HRs.csv, UA_HRs.csv, AW_HRs.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by individual. </p> <p>WS_groups.csv, UA_groups.csv, AW_groups.csv - Home range areas (derived from autocorrelated kernel density estimators) and associated variables, by groups. </p> <p>Note: Raw telemetry data and home range polygons are not available due to the sensitive nature of providing animal locations publicly. Please direct any questions or concerns to the corresponding author (nathan.d.hooven@gmail.com). </p>
Data from Investigating the effects of diurnal and nocturnal pollinators on male and female reproductive success and on floral trait selection in Silene dioica
<p><strong>data_all_OdEx.csv</strong>: all data about phenotypes or reproductive success at the individual scale</p> <ul> <li>ID : ID name</li> <li>nGrSemis_min : seed number needed to be sowned to get enough seedlings</li> <li>nGrGerm : seed number effectively sowned</li> <li>nGrGerm_OK : number of germinated seed</li> <li>TauxGerm : germination rate</li> <li>nFruits_MAX : maximal number of fruit that the plant could have produced</li> <li>nFruits_OK : effective number of fruits that the plant had produced</li> <li>nFruits_OK_avecPred : effective number of fruits that the plant had produced ignoring predation</li> <li>nFruits_pred : number of predated fruits</li> <li>mean_nbSeeds : mean number of seeds per fruit</li> <li>sd_nbSeeds : sd number of seeds per fruit</li> <li>mean_nbOv : mean ovule non fertilize per fruit</li> <li>sd_nbOv : sd ovule non fertilize per fruit</li> <li>mean_nbOvTOT : mean ovule number per flower</li> <li>sd_nbOvTOT : sd ovule number per flower</li> <li>prodTOT : total number of seed produced including germination rate</li> <li>FS : Fruit-set</li> <li>SS : Seed-set</li> <li>prodTOTsg : total number of seed produced without germination rate</li> <li>nbFlo_run0 : flower number at the beginning of the experiment</li> <li>nbFlo_run1 : flower number at the first measurement</li> <li>mean_nbFlo : mean flower number</li> <li>MeanFec : mean seed sired per males according to MEMM model</li> <li>MeanDelta : mean delta pollen dispersion according to MEMM model</li> <li>MeanMRS : mean male reproductive success (including female RS) according to MEMM model</li> <li>MedFec : same as above with the median</li> <li>MedDelta : same as above with the median</li> <li>MedMRS : same as above with the median</li> <li>VarFec : same as above with the variance</li> <li>VarDelta : same as above with the variance</li> <li>VarMRS : same as above with the variance</li> <li>ciFec : Same as above with confidence interval</li> <li>ciDelta : Same as above with confidence interval</li> <li>ciMRS : Same as above with confidence interval</li> <li>MS_Res : mating success</li> <li>mean_lFl : mean corolla width</li> <li>mean_hFl : mean calyx height</li> <li>QttTOT : pollen number per flower</li> <li>pop : which originate population</li> <li>cohort : which cohort</li> </ul> <p><strong>data_seeds_OdEx.csv</strong> : all data about seed number of weight as well as unfertilized ovule at the fruit scale for female RS</p> <ul> <li>ID : ID name</li> <li>noFruit : ID fruit</li> <li>poids : seed weight</li> <li>nbSeeds : number of seeds</li> <li>nbOv : number of unfertilized ovule</li> <li>moySeeds : mean seed size</li> <li>varSeeds : variance in seed size</li> </ul> <p><strong>data_poll_OdEx.csv</strong> : all data about pollinator observation session</p> <ul> <li>ID : ID name</li> <li>session : observation session number</li> <li>nbVis : number of independent insect attracted</li> <li>nbVisTot : number of total visit</li> <li>binVis : individual visited or not</li> </ul>
Datasets for the Results of Scratch Tests of Green Wood and Results of Scratch Tests of Timber Components (D2.1) and Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk (D2.2) of 5G-TIMBER EU Project
<p>7 June 2024: added D2.2_data_statistics.zip and D2.2_analysis_results.zip, which are the datasets for D2.2 "<span>Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</span></p> <p>D2.2 presents the Hygro-Thermo-Mechanical (HTM) models and the finite element (FE) analyses of selected wooden components that use the material properties of wood presented in deliverable D2.1 "Input database for selected wood parts and timber components: material properties, representative environmental conditions,<br>and loads" (see below). </p> <p>------</p> <p>Figures_22_23_24_25.xlsx : Results of Scratch Tests of Green Wood</p> <p>corrected_Figures_26_27_28_29_30.xlsx : Results of results of Scratch Tests of Timber Components (new version, uploaded on 26 October 2023)</p> <p>This dataset consists of 2 Excel files that correspond to the scratch test results reported in the deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</p> <p>The purpose of D2.1, to which this dataset is related, is to present the input data needed for the Hygro-Thermo-Mechanical (HTM) models and the related finite element (FE) analyses planned for a follow-up deliverable, i.e., the D2.2. (Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains And Stresses And Crack Risk). The data include the material properties for green wood and selected wooden components, as well as the plans to collect environmental conditions and loads to be considered in the analyses for prediction of the crack risk of timber components under moisture variations. In additions, new results of scratch tests of wood and wooden components, supported by computed tomography (CT) investigations, are collected to define a model for shear failure risk to be added to the HTM computational models.</p> <p>In D2.1, scratch tests carried out at VTT are described and their results are collected to provide information about the moisture effects of wood logs during cutting operations in sawmills, as well as on relevant fracture and shear properties for wooden components in sawing centres before using them to produce wooden elements of modular buildings in the production. The scratch tests are supported by CT tomography investigations and these results are also reported in the deliverable.</p> <p>D2.1 is available here: <a title="Deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" " href="../records/10577505" target="_blank" rel="noopener">https://zenodo.org/records/10577505</a> </p>
pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection (Supp data)
<p>Supplemental tables for article: <strong>pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection </strong></p>
Data from: Empirical verification of feeding selectivity of larval and juvenile pelagic fishes using in situ zooplankton communities
<p>Most studies on the feeding ecology of larvae and juveniles of commercially important pelagic fishes have used field-based approaches. However, due to possible biases related to net sampling, it is uncertain whether the results obtained from those studies truly represent the situation of live fish in the sea. Here we investigated the feeding ecology of pelagic fishes through a laboratory experiment minimizing the biases inherent in field net sampling. In the experiment, hatchery-reared juvenile chub mackerel (<em>Scomber japonicus</em>) and larval/juvenile Japanese anchovy <em>(Engraulis japonicus</em>) were fed with wild-caught zooplankton assemblages collected from around Hakatajima Island in the Seto Inland Sea, Japan. The relationships between fish size and prey number in the gut, and the selectivity on each prey organism were determined. As a result, in both species, prey number and size increased with body size, and the fish showed strong selectivity for crustaceans including copepodites and adults of copepods. Our data has also clearly indicated that both species can selectively prey on preferred foods that are rare while avoiding non-preferred foods that are abundant. These results, which substantially accord with reports from previous field studies, will not only help field scientists make a convincing interpretation of their data, but also open the possibility of further laboratory studies on detailed mechanisms of the feeding selectivity of larval/juvenile pelagic fishes.</p>
Pesticide exposure triggers sex-specific inter- and trans-generational effects conditioned by past sexual selection
<p>Environmental variation often induces plastic responses in organisms that can trigger changes in subsequent generations through non-genetic inheritance mechanisms. Such transgenerational plasticity thus consists of environmentally-induced non-random phenotypic modifications that are transmitted through generations. Transgenerational effects may vary according to the sex of the organism experiencing the environmental perturbation, the sex of their descendants, or both, but whether they are affected by past sexual selection is unknown. Here we use experimental evolution on an insect model system to conduct a first test of the involvement of sexual selection history in shaping transgenerational plasticity in the face of rapid environmental change (exposure to pesticides). We manipulated evolutionary history in terms of the intensity of sexual selection for over 80 generations before exposing individuals to the toxicant. We found that sexual selection history constrained adaptation under rapid environmental change. We also detected intergenerational and transgenerational effects of pesticide exposure in the form of increased fitness and longevity. These cross-generational influences of toxicants were sex-dependent (they affected only male descendants), and intergenerational, but not transgenerational, plasticity was modulated by sexual selection history. Our results highlight the complexity of intragenerational, intergenerational, and transgenerational influences of past selection and environmental stress on phenotypic expression.</p>
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Data from: Meta-analytical evidence for frequency-dependent selection across the tree of life
<p>Explaining the maintenance of genetic variation in fitness related traits within populations is a fundamental challenge in ecology and evolutionary biology. Frequency-dependent selection (FDS) is one mechanism that can maintain such variation, especially when selection favours rare variants (negative FDS). However, our general knowledge about the occurrence of FDS, its strength and direction remain fragmented, limiting general inferences about this important evolutionary process. We systematically reviewed the published literature on FDS and assembled a database of 747 effect sizes from 101 studies to analyse the occurrence, strength, and direction of FDS, and the factors that could explain heterogeneity in FDS. Using a meta-analysis, we found that overall, FDS is more commonly negative, although not significantly when accounting for phylogeny. An analysis of absolute values of effect sizes, however, revealed the widespread occurrence of modest FDS. However, negative FDS was only significant in laboratory experiments and non-significant in mesocosms and field-based studies. Moreover, negative FDS was stronger in studies measuring fecundity and involving resource competition over studies using other fitness components or focused on other ecological interactions. Our study unveils key general patterns of FDS and points in future promising research directions that can help us understand a long-standing fundamental problem in evolutionary biology and its consequences for demography and ecological dynamics.</p>
Figure 3 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes
Figure 3. Aboveground biomass (g m−2) across densities (plants m−2) for maize and Setorio foberi. Vertical arrows indicate the maize (black numbers) and S. foberi (gray numbers) densities at which S. foberi reaches inflection point and 90% of maximum biomass. Horizontal arrows indicate S. foberi biomass at inflection point and 90% of maximum biomass.
Figure 4 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes
Figure 4. Relative biomass of Amoronthus hybridus:maize replacement series from experiment 1 (left) and experiment 2 (right) with densities based in inflection point (A and B), maximum biomass (C and D), and equal N uptake (E and F). Black circles represent maize relative biomass,white circles represent A. hybridus relative biomass,and gray diamonds represent relative yield total biomass (RYT). Dotted line represents a relative biomass of 1 for all the proportions. The points and error bars represent data means and standard errors.
Figure 2 in Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes
Figure 2. Aboveground biomass (g m−2) across densities (plants m−2) for maize and Amoronthus hybridus. Vertical arrows indicate the maize (black numbers) and A. hybridus (gray numbers) densities at which A. hybridus reaches inflection point and 90% of maximum biomass. Horizontal arrows indicate A. hybridus biomass at the inflection point and 90% of maximum biomass.
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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.