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2,911 results for “dispersal”
Seed Dispersal and Seedling Establishment of Sarracenia Purpurea at Hawley Bog, MA 1998-1999
Plant ecologists continue to grapple with Reid’s paradox, the observation that dispersal distances of most herbs and trees are too limited to account for their recolonization of northern latitudes following glacial recession. As global climate changes and natural habitats become increasingly fragmented, understanding patterns of seed dispersal and the potential for long-distance colonization takes on new importance. We studied the dispersal and establishment of the northern pitcher plant Sarracenia purpurea, which grows commonly in isolated bogs throughout Canada and eastern North America. Median dispersal distance of S. purpurea is only 5 cm, which is insufficient to explain its occurrence throughout formerly glaciated regions of North America. Establishment probability of seeds in the field is approximately 5%, and juveniles are normally found clustered around adult plants. The large-scale population genetic structure of this species can be accounted for by rare long-distance dispersal events, but its predictable occurrence in isolated habitats requires additional explanation. Reid’s paradox remains an open question, and predicting long-range colonization into fragmented habitats by species with limited dispersal ability is a novel challenge.
Tree Seed Dispersal in Hemlock Removal Experiment at Harvard Forest 2005
Throughout the northeast, the hemlock woolly adelgid (Adelges tsugae) threatens eastern hemlock (Tsuga canadensis) through direct mortality resulting from infestation followed by defoliation and indirect mortality in the form of pre-emptive logging. The efficacy of regeneration of vegetation following hemlock decline depends upon advance regeneration of seedlings and saplings, seed dispersal, and recruitment. In this study, we investigated (1) whether the basic parameters of height of release and wind velocity affected seed dispersal distance and (2) tested the fit of a basic ballistic model of seed dispersal to empirical data in areas both with and without canopies. We collected empirical data from seed dropping and seed rain experiments at Harvard Forest. Height and wind velocity only affected seed dispersal distance in open areas. Predicted values of dispersal distance generated by the basic ballistic model did not provide a good fit to observed dispersal data. Poor fits of the ballistic model to the data were due to the model’s inability to account for rare, long distance dispersal events. More complex models with additional parameters are necessary to model non-localized seed dispersal.
Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India
<p>This dataset contains compiled Fruit, seed dispersal, and life history traits of tropical rainforest trees of the Anamalai Hills, Western Ghats, India. The list of species included are mainly from the following two related publications:<br>- Muthuramkumar, S., Ayyappan, N., Parthasarathy, N., Mudappa, D., Raman, T.R.S., Selwyn, M.A. and Pragasan, L.A. (2006), <a href="https://doi.org/10.1111/j.1744-7429.2006.00118.x">Plant Community Structure in Tropical Rain Forest Fragments of the Western Ghats, India</a>. <em>Biotropica</em>, 38: 143-160. https://doi.org/10.1111/j.1744-7429.2006.00118.x<br>- Osuri, A., Chakravarthy, D., Mudappa, D., Raman, T., Ayyappan, N., Muthuramkumar, S., & Parthasarathy, N. (2017). <a href="http://httpd//doi.org/10.1017/S0266467417000219">Successional status, seed dispersal mode and overstorey species influence tree regeneration in tropical rain-forest fragments in Western Ghats, India</a>. <em>Journal of Tropical Ecology</em>, 33(4), 270-284. doi:10.1017/S0266467417000219<br>The present dataset is an expanded and updated version of the related dataset available at <a href="https://doi.org/10.5061/dryad.vd0nn">https://doi.org/10.5061/dryad.vd0nn</a><br> <br>Species traits information was collated from <a href="http://www.biotik.org/">BIOTIK (http://www.biotik.org/</a>), <a href="http://www.flowersofindia.net/">Flowers of India (http://www.flowersofindia.net/)</a>, India Biodiversity Portal (http://indiabiodiversity.org/), <a href="https://doi.org/10.5061/dryad.234/1">Global wood density database (https://doi.org/10.5061/dryad.234/1)</a> and <a href="https://doi.org/10.1017/S0266467417000219">Osuri et al. (2014): https://doi.org/10.1017/S0266467417000219</a>. We also referred to the following previous studies that provided information on the successional status of rain-forest species in the Western Ghats (Chetana 2013, Pascal 1988, Raman et al. 2009, Sreejith 2005).</p> <p><strong>References:</strong><br>CHETANA, H. C. 2013. Assessing the ecological processes in abandoned tea plantations and its implication for ecological restoration in the Western Ghats, India. PhD thesis, Manipal University.<br>OSURI, A. M., KUMAR, V. S. & SANKARAN, M. 2014. Altered stand structure and tree allometry reduce carbon storage in evergreen forest fragments in India’s Western Ghats. <em>Forest Ecology and Management </em>329: 375–383.<br>PASCAL, J. P. 1988. <em>Wet evergreen forests of the Western Ghats of India: Ecology, structure, floristic composition and succession</em>. Institut Français de Pondichéry, Pondicherry.<br>RAMAN, T. R. S., MUDAPPA, D. & KAPOOR, V. 2009. Restoring rainforest fragments: survival of mixed-native species seedlings under contrasting site conditions in the Western Ghats, India. <em>Restoration Ecology</em> 17:137–147.<br>SREEJITH, K. A. 2005. Ecological and ecophysiological studies on the successional status of tree seedlings in tropical wet evergreen and semi-evergreen forests of Kerala. PhD thesis, Forest Research Institute, Dehradun.</p> <p><strong>Geographic Coverage:</strong><br>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br>1. Begins: 2003-03-01 (Year, Month, Day)<br>2. Ends: 2024-02-10 (Year, Month, Day)</p> <p>Besides the <strong>README.txt</strong> file, the dataset includes the following comma-delimited text (csv) file with the data in columns as explained below:</p> <p><strong>Anamalai_tree_traits_2024.csv</strong></p> <p><strong>spec_name_ORIG:</strong> Scientific name of the species used during the data collection<br><strong>genus:</strong> Genus of the taxon<br><strong>specificEpithet:</strong> Specific epithet of the taxon in the Latin binomial name<br><strong>Accept_name_WFO:</strong> Updated scientific name of the species as in Plants of the World Online (POWO, https://powo.science.kew.org/)<br><strong>Habit:</strong> life form of the species(tree/shrub/cane/palm)<br><strong>Distribution:</strong> Distribution of the species in the study area (Native/Endemic/Introduced)<br><strong>IUCN_status:</strong> IUCN status of the species (CR-Critically Endangered,DD-Data deficient,EN-Endangered,LC-Least Concern,NT-Near Threatened,VU-Vulnerable,NA-Unknown)<br><strong>Wden_final:</strong> Wood density value assigned for the species (g cm^-3); NA - not available; sourced from Global wood density database (https://doi.org/10.5061/dryad.234/1)<br><strong>wd_level:</strong> Level in which the wood density value belongs (Species - wood density value is from species level; genus - wood density value assigned is the genus level average value)<br><strong>fruit_type:</strong> Morphological type of fruit<br><strong>fleshy_dry:</strong> Whether fruit is a dry fruit or fleshy, with aril or other parts <br><strong>seed_size:</strong> Species seed size: L = Large (>3 cm); M = Medium (1-3 cm); S = Small (<1 cm)<br><strong>disperser:</strong> Categories indicating seed dispersal mode: Bird, mammal, bird and mammal (Mammal_bird), gravity, wind, or unknown<br><strong>habitat:</strong> Habitat affinity category: EG_edg - evergreen forest edge; EG_for - evergreen forest; Dec_for - deciduous forest; Int – Introduced species; Unknown – Unknown<br><strong>habt_new:</strong> Habitat affinity new category: Mature – mature forest; Secondary – secondary forest, NA - unknown/Introduced species<br><strong>ad_ht:</strong> Species maximum adult height (m)</p>
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions
<p>Dataset from the publication: <strong>Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions</strong>. DOI: <a href="https://doi.org/10.5194/egusphere-2022-230">10.5194/egusphere-2022-230</a></p> <p> </p> <p>Includes: <em><strong>EGFs, Dispersion Curves measurements, RFs, Vs3dmodel and Moho depths</strong></em></p> <p> </p>
European_bird_dispersal: v1.0.3-Edispersal
<p><strong>Standardised empirical dispersal kernels emphasise the pervasiveness of long-distance dispersal in European birds </strong></p> <p>ABSTRACT:</p> <ol> <li>Dispersal is a key life-history trait for most species and is essential to ensure connectivity and gene flow between populations and facilitate population viability in variable environments. Despite the increasing importance of range shifts due to global change, dispersal has proved difficult to quantify, limiting empirical understanding of this phenotypic trait and wider synthesis. </li> <li>Here we introduce a statistical framework to estimate standardised dispersal kernels from biased data. Based on this, we compare empirical dispersal kernels for European breeding birds considering age (average dispersal; natal, before first breeding; and breeding dispersal, between subsequent breeding attempts) and sex (females and males) and test whether different dispersal properties are phylogenetically conserved. </li> <li>We standardised and analysed data from an extensive volunteer-based bird ring-recoveries database in Europe (EURING) by accounting for biases related to different censoring thresholds in reporting between countries and to migratory movements. Then, we fitted four widely used probability density functions in a Bayesian framework to compare and provide the best statistical descriptions of the different age and sex-specific dispersal kernels for each bird species. </li> <li>The dispersal movements of the 234 European bird species analysed were statistically best explained by heavy-tailed kernels, meaning that while most individuals disperse over short distances, long-distance dispersal is a prevalent phenomenon in almost all bird species. The phylogenetic signal in both median and long dispersal distances estimated from the best-fitted kernel was low (Pagel’s λ < 0.25), while it reached high values (Pagel’s λ >0.7) when comparing dispersal distance estimates for fat-tailed dispersal kernels. As expected in birds, natal dispersal was on average 5 km greater than breeding dispersal, but sex-biased dispersal was not detected.</li> <li>Our robust analytical framework allows sound use of widely available mark-recapture data in standardised dispersal estimates. We found strong evidence that long-distance dispersal is common among European breeding bird species and across life stages. The dispersal estimates offer a first guide to selecting appropriate dispersal kernels in range expansion studies and provide new avenues to improve our understanding of the mechanisms and rules underlying dispersal events. </li> </ol> <p><strong>Content</strong></p> <ul> <li>The workflow for estimating dispersal kernels from ring-recovery data for all of Europe</li> <li>The code to develop the dispersal kernels with ring-recovery data. </li> <li>Dispersal distances for European birds: <a href="/api/files/0afdcc68-9a21-4e61-b8ff-9eb74f3c0d70/Table_S14_ species_dispersal_distances_v1_0_2.csv?versionId=1e3c628b-88c0-4b20-aad7-a5bf06e21bec">Table_S14_ species_dispersal_distances_v1_0_2.csv</a></li> <li>Dispersal kernel parameters for European birds: <a href="/api/files/0afdcc68-9a21-4e61-b8ff-9eb74f3c0d70/Table_S13_species_dispersal_parameters_v1_0_2.csv?versionId=195bd982-9d5e-47a1-b7d8-cf5a101f3603">Table_S13_species_dispersal_parameters_v1_0_2.csv</a></li> </ul>
Dataset for Precursor Nuclearity and Ligand Effects in Atomically-Dispersed Heterogeneous Iron Catalysts for Alkyne Semi-Hydrogenation
<p>This dataset complements the publication entitled "Precursor Nuclearity and Ligand Effects in Atomically-Dispersed Heterogeneous Iron Catalysts for Alkyne Semi-Hydrogenation" by Dario Faust Akl, Andrea Ruiz-Ferrando, Dr. Edvin Fako, Dr. Roland Hauert, Dr. Olga Safonova, Dr. Sharon Mitchell, Prof. Núria López, Prof. Javier Pérez-Ramírez. Please refer to the Readme.txt file for information about the file structure and content.<br> </p>
Protist Dispersal Detection: University of Michigan Biological Station, July 2024
This dataset contains the results of a field dispersal array assembled in Gates Bog, Pellston, Michigan. The data were collected by a graduate student, and consist of measurements of protist presence or absence in 1mL fluid samples taken from pitcher plants and centrifuge tubes in the array. The dataset contains both initial protist detection from the fluid samples, as well as detection after a 24 hour incubation period. The dataset also contains the positions of each plant and tube used for sample collection and their distances from the established source population at the center of the array. We used the purple pitcher plant, Sarracenia purpurea, as a model system to explore questions of specialist protist dispersal. Newly opened pitchers are sterile, providing virgin habitat open to community assembly of highly specialized protist species (Peterson 2008). The placement of a known community of protists at the center of an uncolonized array of habitat patches allows us to identify both sources and destinations of dispersing microbes in the array. The purpose of this study is to measure dispersal rates for a subset of pitcher plant protist species.
Kelp metapopulations: Semi-annual time series of spore dispersal times among giant kelp patches in southern California, 1996 - 2006
These data describe the estimated dispersal duration of spores of giant kelp, Macrocystis pyrifera, among patches in southern California, USA, from 1996 to 2006. Asymmetrical and dynamic estimates of giant kelp spore dispersal durations among patches were estimated for 6-month periods (January - June and July - Dececember, 1996 - 2006) using minimum mean transit times connecting source and destination connectivity cells in a high-resolution, three-dimensional, spatiotemporally-explicit ocean circulation model (Regional Oceanic Modeling System, ROMS). Minimum transport times between giant kelp patches were assumed to be proportional to minimum transport times between ROMS cells and the alongshore distance between giant kelp patches
Raw data for "The role of conidia in the dispersal of *Ascochyta rabiei*"
<p>Raw data associated with the pre-print, “<em>The role of conidia in the dispersal of </em>Ascochyta rabiei”, <a href="https://doi.org/10.1101/2020.05.12.091827">https://doi.org/10.1101/2020.05.12.091827</a></p> <p>There are six data files associated with this manuscript. Five are raw data, one was generated as a course of the analysis, “weather_summary.csv”. All files are in .csv format. Details for each including the number of columns and column contents and units follow.</p> <p><strong>Files and Content Descriptions</strong></p> <ul> <li><strong>BCG_weather_data.csv</strong> – 15-minute interval weather data from the Birchip Ag Group automated weather station near Curyo, Victoria, Australia for the time period of 01/10/2019 to 31/10/2019</li> <li><strong>Curyo_SPA_2019_weather.csv </strong>– 10-minute interval weather data from AgVictoria’s automated weather station at Curyo, Victoria, Australia from 22/01/2019 to 06/12/2019 recorded with Measurement Engineering Australia, Adelaide, Australia equipment</li> <li><strong>Dispersal_experiment_dates.csv</strong> – Data detailing each spread event at each location including the time trap plants were put out and brought in and date assessed</li> <li><strong>Horsham_SPA_2019_weather.csv</strong> – 10-minute interval weather data from AgVictoria’s automated weather station at Horsham, Victoria, Australia from 01/01/2019 to 06/12/2019 recorded with Measurement Engineering Australia, Adelaide, Australia equipment</li> <li><strong>lesion_counts.csv </strong>– Data detailing lesion counts on trap plants for each location and spread event</li> <li><strong>weather_summary.csv</strong>– Summary weather data detailing for each location and spread event</li> </ul> <p><em><strong>BCG_weather_data.csv </strong></em>The file “BCG_weather_data.csv” contains six columns:</p> <ul> <li><strong>Reading Time </strong>– the time at which the data was recorded</li> <li><strong>Rainfall</strong> – the amount of rainfall (mm)</li> <li><strong>Humidity</strong> – relative humidity (%)</li> <li><strong>Temperature</strong> – air temperature (˚C)</li> <li><strong>Wind Speed</strong> – wind speed (km/p)</li> <li><strong>Wind Direction</strong> – direction in which the wind was blowing from (cardinal directions)</li> </ul> <p><em><strong>Curyo_SPA_2019_weather.csv</strong></em> and H<em><strong>orsham_SPA_2019_weather.csv </strong></em>The files “Curyo_SPA_2019_weather.csv” and “Horsham_SPA_2019_weather.csv” both contain 22 columns:</p> <ul> <li><strong>Time</strong></li> <li><strong>Air Temperature - average (ºC)</strong></li> <li><strong>Soil Temperature - average (ºC)</strong></li> <li><strong>Relative Humidity - average (%)</strong></li> <li><strong>Wind Speed - minimum (km/h)</strong></li> <li><strong>Wind Speed - average (km/h)</strong></li> <li><strong>Wind Speed - maximum (km/h)</strong></li> <li><strong>Wind Direction - average (º)</strong></li> <li><strong>Solar Radiation - average (W/m^2)</strong></li> <li><strong>Sigma - average (deg)</strong></li> <li><strong>Rainfall - (mm)</strong></li> <li><strong>Voltage - minimum (V)</strong></li> <li><strong>Voltage - average (V)</strong></li> <li><strong>Voltage - maximum (V)</strong></li> <li><strong>Apparent Temperature - average (ºC)</strong></li> <li><strong>Dew Point - average (ºC)</strong></li> <li><strong>Delta T - average (ºC)</strong></li> </ul> <p><em><strong>Dispersal_experiment_dates.csv </strong></em>The file “Dispersal_experiment_dates.csv” contains five columns:</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep </strong>– Spread event number for each location</li> <li><strong>time out </strong>– date and time on which the trap plants were deployed in the paddock for the spread event (rainfall)</li> <li><strong>time removed </strong>– date and time on which the trap plants were retrieved from the paddock after the spread event (rainfall)</li> <li><strong>assessment date</strong>– date on which trap plants were assessed for number of lesions</li> </ul> <p><em><strong>lesion_counts.csv </strong></em>The file "lesion_counts.csv” contains thirteen columns:</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep </strong>– Spread event number for each location</li> <li><strong>distance </strong>– Distance from infection source (m)</li> <li><strong>station – </strong>Trap plant units (number of trap plants at each point along transect)</li> <li><strong>transect – </strong>One of ten repeating lines along which the trap plant stations were deployed in a 90˚ arc downwind of the infection source</li> <li><strong>dist_stat – </strong>A concatenation of the ‘dist’ and ‘station’ columns</li> <li><strong>plant_no – </strong>Total number of plants at a given station</li> <li><strong>pot_no </strong>– Individually assigned pot number for each individual transect (1 – 56)</li> <li><strong>counts_p1 – </strong>Lesion counts for Pot 1</li> <li><strong>counts_p2 – </strong>Lesion counts for Pot 2</li> <li><strong>counts_p3 – </strong>Lesion counts for Pot 3</li> <li><strong>counts_p4 – </strong>Lesion counts for Pot 4</li> <li><strong>counts_p5 – </strong>Lesion counts for Pot 5</li> </ul> <p><em><strong>weather_summary.csv </strong></em>The file “weather_summary.csv” contains six columns that summarise the weather conditions during each of the six spread events at three experiment plot locations.</p> <ul> <li><strong>site </strong>– The experiment location</li> <li><strong>rep</strong> – Spread event number for each location</li> <li><strong>mws</strong> – Mean wind speed for the spread event (m/s)</li> <li><strong>ws_sd </strong>– Wind speed standard deviation for the spread event</li> <li><strong>mwd </strong>– Mean wind direction for the spread event (˚)</li> <li><strong>sum_</strong>rain – Total precipitation during spread event including both natural rainfall and overhead sprinkler irrigation (mm)</li> </ul>
Supplementary material from "Experimentally disentangling intrinsic and extrinsic drivers of natal dispersal in a nocturnal raptor"
<p><strong>Abstract</strong></p> <p>Equivocal knowledge of the phase-specific drivers of natal dispersal remains a major deficit in understanding causes and consequences of dispersal and thus, spatial dynamics within and between populations. We performed a field experiment combining partial cross-fostering of nestlings and nestling food supplementation in little owls (<em>Athene noctua</em>). This approach disentangled the effect of nestling origin from the effect of the rearing environment on dispersal behaviour, while simultaneously investigating the effect of food availability in the rearing environment. We radio-tracked fledglings to quantify the timing of pre-emigration forays and emigration, foray and transfer duration, and the dispersal distances. Dispersal characteristics of the pre-emigration phase were affected by the rearing environment rather than by the origin of nestlings. In food-poor habitats, supplemented individuals emigrated later than unsupplemented individuals. By contrast, transfer duration and distance were influenced by the birds' origin rather than by their rearing environment. We found no correlation between timing of emigration and transfer duration or distance. We conclude that food supply to the nestlings and other characteristics of the rearing environment modulate the timing of emigration, while innate traits associated with the nestling origin affect the transfer phases after emigration. The dispersal behaviours of juveniles prior and after emigration, therefore, were related to different determinants, and are suggested to form different life-history traits.</p>
Data from: Behavioural responses to potential dispersal cues in two economically important cereal-feeding eriophyoid mite species
<p>Variables:</p> <ol> <li>species (ABH = <em>Abacarus hystrix</em>, WCM = <em>Aceria tosichella</em> MT1 genetic lineage)</li> <li>variant - experimental treatment (type of dispersal cue): wind, an insect vector, presence of a fresh plant</li> <li>feeding - no. of feeding specimens</li> <li>walking - no. of walking specimens</li> <li>standing - no. of specimens standing vertically</li> <li>cha - no. of specimens forming chains</li> <li>mob - no. of specimens capable to move</li> <li>pop - no. of all specimens (including quiescent stages)</li> </ol>
Quantifying wind-driven dispersal of zooplankton in a Mediterranean pond
<p>Dispersal is an essential component in the life history of organisms and has strong ecological implications. Although it has been assumed that small organisms have very high dispersal rates, quantitative data supporting this claim remains scarce. In the context of zooplankton, wind stands out as a primary vector facilitating passive dispersal. We quantified short-distance wind-driven dispersal of propagules across various zooplankton taxa in a Spanish Mediterranean coastal temporary pond. We have also studied dispersal patterns in relation to the wind regime (intensity, steadiness and gust direction), source pond status (volume of water) and demographic dynamics (the abundance of individuals in water column populations). Further, we related propagules size merurements (surface, L2 and volume, L3) with dispersal distances. Additionally, we have performed measurements of the dispersed propagules and investigated the relationship between their dimensions and the dispersive distance on a local scale. Here, we present the raw data gathered during research.</p>
Phlorest phylogeny derived from Grollemund et al. 2015 'Bantu expansion shows habitat alters the route and pace of human dispersals'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Grollemund R, Branford S, Bostoen K, Meade A, Venditti C & Pagel M. 2015. Bantu expansion shows habitat alters the route and pace of human dispersals. Proceedings of the National Academy of Sciences of the USA, 112(43), 13296-13301.</p> </blockquote>
Influence of cation concentration and valence on the structure and texture of spray-dried supraparticles from colloidal silica dispersions
<p>These datasets display the raw data for the manuscript: Huanhuan Zhou, Philipp Groppe, Thomas Zimmermann, Susanne Wintzheimer, Karl Mandel, Influence of cation concentration and valence on the structure and texture of spray-dried supraparticles from colloidal silica dispersions, Journal of Colloid and Interface Science, Volume 658,<br>2024, Pages 199-208, https://doi.org/10.1016/j.jcis.2023.12.051.</p> <p>The data connection file serves as an explanation for all datasets and their connection to the data displayed in the manuscript.</p>
Data from: Effects of dispersal and geomorphology on riparian seedbanks and vegetation in a boreal stream
<p>SiteData: information that describes 20 riparian zones along Svartån, a boreal free-flowing stream, indicated per LocationID (column A). Coordinates are given in SWEREF 99 TM (column B and C) and degrees of longitude and latitude (column D and E). RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Side of stream indicates plot placement when looking towards downstream. Data collection is described in the paper linked to below. </p> <p> </p> <p>LitterData: information that describes species lists of litter seedbanks from 20 riparian sites. Litter samples were taken in an unstandardised manner at each location. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing.</p> <p> </p> <p>SeedData: information that describes the soil seedbank composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Layer refers to samples that are taken from from layer 0-1 cm in the soil, 1-5 cm or from 5-10 cm deep. Data collection is described in the paper linked to below. </p> <p> </p> <p>VegetationData: information that describes vegetation composition from 20 riparian sites. LocationID refers to locations as described in file SiteData, RPD refers to River Process Domain and takes one of three forms: lake, rapid or slow-flowing. Abundance is indicated following the categories in Table 1. Data collection is described in the paper linked to below. </p> <p> </p> <p>Table 1. Vegetation cover classes.</p> <table> <tbody> <tr> <td> <p><strong>Code</strong></p> </td> <td> <p><strong>Cover (%)</strong></p> </td> </tr> <tr> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p><1</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>1-3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>3-5</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>5-15</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>15-25</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>25-50</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>50-75</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>75-100</p> </td> </tr> </tbody> </table> <p> </p> <p>For more information, help or collaboration, please contact Jacqueline.Hoppenreijs@kau.se. If you use the data here in your work or research, please cite the publication appropriately.</p>
A Terrylene Bisimide based Universal Host for Aromatic Guests to Derive Contact Surface-Dependent Dispersion Energies
<p>Additional data to report <a href="https://doi.org/10.1002/anie.202318451">https://doi.org/10.1002/anie.202318451</a>:<br><br>π–π interactions are among the most important intermolecular interactions in supramolecular systems. Here we determine experimentally a universal parameter for their strength that is simply based on the size of the interacting contact surfaces. Toward this goal we designed a new cyclophane based on terrylene bisimide (TBI) π-walls connected by <em>para</em>-xylylene spacer units. With its extended π-surface this cyclophane proved to be an excellent and universal host for the complexation of π-conjugated guests, including small and large polycyclic aromatic hydrocarbons (PAHs) as well as dye molecules. The observed binding constants range up to 10<sup>8</sup> M<sup>−1</sup> and show a linear dependence on the 2D area size of the guest molecules. This correlation can be used for the prediction of binding constants and for the design of new host–guest systems based on the herewith derived universal Gibbs interaction energy parameter of 0.31 kJ/molÅ<sup>2</sup> in chloroform.</p>
Data for article: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils
<p>Supplementary information for:</p> <p><strong>A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils</strong></p> <p>Torsten Hauffe, Mathias M. Pires, Tiago B. Quental, Thomas Wilke, and Daniele Silvestro</p> <p> </p><ul> <li> Simulations <ul> <li>Scripts <ul> <li>Scenario1_SamplingHeterogeneity.R: Script to simulate biogeographic histories with sampling heterogeneity</li> <li>Scenario3_SealevelInvasion.R: Script to simulate biogeographic histories where sea level facilitates dispersal and invasion induces extinction</li> <li>Scenario3_DiversityDependence.R: Script to simulate diversity-dependent biogeographic histories</li> <li>Scenario4_TraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> <li>Scenario5_CategoricalTraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> </ul> </li> <li>Results <ul> <li>Scenario1_SamplingHeterogenetiy_alpha05.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 0.5</li> <li>Scenario1_SamplingHeterogenetiy_alpha1.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 1</li> <li>Scenario1_SamplingHeterogenetiy_alpha2.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 2</li> <li>Scenario1_SamplingHeterogenetiy_alpha10.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 10</li> <li>Scenario2_Independent_dispersal_and_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_independent_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_independent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and invasion induced extinction</li> <li>Scenario3_Independent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-independent dispersal and extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_independent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Independent_dispersal_and_Diversity_dependent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and extinction</li> <li>Scenario4_Independent_dispersal_and_extinction.txt: Results of scenario 4 with trait-independent dispersal and extinction</li> <li>Scenario4_Trait_dependent_dispersal_and_independent_extinction.txt: Results of scenario 4 with trait-dependent dispersal and independent extinction</li> <li>Scenario4_Independent_dispersal_and_trait_dependent_extinction.txt: Results of scenario 4 with independent dispersal and trait-dependent extinction</li> <li>Scenario4_trait_dependent_dispersal_and_extinction.txt: Results of scenario 4 with trait-dependent dispersal and extinction</li> <li>Scenario5_CatTrait_dependent_dispersal_and_independent_extinction.txt: Results of model 2 with categorical traits (e.g family) influence dispersal but no influence of a category-specific continuous traits</li> </ul> </li> </ul> </li> <li>Carnivora <ul> <li>BinnedOccurrence: Folder with 100 replicates of binned occurrences of max. 330 carnivoran genera throughout the Neogene</li> <li>BodyMass: Folder with 100 replicates of body mass for 330 carnivoran genera</li> <li>Sealevel: Folder with sea level through the Neogene</li> <li>Temperature: Folder with the temperature record of the Neogene</li> <li>Families: Folder with families as taxonomic proxy for phylogeny. FamilyGeneraNumeric.txt is the numeric coding used for the Bayesian analyses of carnivoran biogeography</li> </ul> </li> </ul> <p></p>
Dataset for "Effects of Fracture Connectivity on Rayleigh Wave Dispersion"
<p>The scripts on the main directory reproduce figures 3 to 11 in the paper. The dataset is separated into two folders which should be extracted to the directory of the scripts, frac_dist and parrot_output. frac_dist contains .mat files with the fracture distribution of samples and parrot_output contains the results from the upscaling procedure (Favino et al., 2020). A summary of each code is provided in the readme.</p>
Even short‐distance dispersal over a barrier can affect genetic differentiation in Gyraulus, an island freshwater snail
<p>Supplementary dataset for a published paper, "Saito T., Sasaki T., Tsunamoto Y., Uchida S., Satake K., Suyama Y., <em>et al.</em> (2022). Even short‐distance dispersal over a barrier can affect genetic differentiation in <em>Gyraulus</em> , an island freshwater snail. <em>Freshwater Biology</em> <strong>67</strong>, 1971–1983. <a href="https://doi.org/10.1111/fwb.13990">https://doi.org/10.1111/fwb.13990</a>"</p>
ScienceDex guides
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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.