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799 results for “abundance data”
Fig. 2 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 2. Distribution of Muschelkalk ammonoid localities used in this study plotted on a map of modern Germany. The overall geographic spread of localities does not change greatly over time.
Fig. 3 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 3. Correlations between richness per map and number of occurrences. A. om7 interval. B. om8 interval. C. om9 interval.
Fig. 1 in Using abundance data to assess the relative role of sampling biases and evolutionary radiations in Upper Muschelkalk ammonoids
Fig. 1. Chart of stratigraphic interval names and durations for the Muschelkalk of the Germanic Basin with ammonoid immigration events marked (simplified from Klug et al. 2005: fig. 1).
Text-fig. 3 Ternary diagram of the relative abundance (in %) of juvenile, prime adult, and old adult specimens in samples of Castor fiber (data from Table 3). The red dots indicate the Pleistocene samples of Bilzingsleben II (B), Weimar- Ehringsdorf (E), and Weimar-Taubach (T), the black dot represents an extant population from Telemark in Norway (data from Campbell 2009). Abbreviations of zones (after Discamps and Costamagno 2015): JOP – Juveniles-Old-Prime dominated zone, JPO – Juveniles-Prime-Old dominated zone, O – Old dominated zone, P – Prime dominated zone. The diagram shows the position of all three fossil samples in the prime dominated zone. in Mortality Profiles Of Castor And Trogontherium (Mammalia: Rodentia, Castoridae), With Notes On The Site Formation Of The Mid-Pleistocene Hominin Locality Bilzingsleben Ii (Thuringia, Central Germany)
Text-fig. 3 Ternary diagram of the relative abundance (in %) of juvenile, prime adult, and old adult specimens in samples of Castor fiber (data from Table 3). The red dots indicate the Pleistocene samples of Bilzingsleben II (B), Weimar- Ehringsdorf (E), and Weimar-Taubach (T), the black dot represents an extant population from Telemark in Norway (data from Campbell 2009). Abbreviations of zones (after Discamps and Costamagno 2015): JOP – Juveniles-Old-Prime dominated zone, JPO – Juveniles-Prime-Old dominated zone, O – Old dominated zone, P – Prime dominated zone. The diagram shows the position of all three fossil samples in the prime dominated zone.
Text-fig. 4. Mortality profile (relative abundance of age groups) of Trogontherium based on dp4 and p4 from the fossil sites of Bilzingsleben II, Mosbach 2 and Tegelen. Data from Table 4. in Mortality Profiles Of Castor And Trogontherium (Mammalia: Rodentia, Castoridae), With Notes On The Site Formation Of The Mid-Pleistocene Hominin Locality Bilzingsleben Ii (Thuringia, Central Germany)
Text-fig. 4. Mortality profile (relative abundance of age groups) of Trogontherium based on dp4 and p4 from the fossil sites of Bilzingsleben II, Mosbach 2 and Tegelen. Data from Table 4.
Data for: Higher floral richness promotes rarer bee communities across remnant and reconstructed tallgrass prairies, though remnants contain higher abundances of a threatened bumble bee (Bombus Latreille)
<p>Managing and restoring tallgrass prairie ecosystem is an important form of pollinator conservation in the Midwestern United States. Prairie reconstruction has been found to enhance native bee diversity and abundance, but it is less clear if prairie reconstruction conserves species thought to be at-risk. We reanalyze a previously published dataset on the bee communities of reconstructed and remnant prairie in the US state of Minnesota to investigate how the abundance of at-risk species respond to local factors, such as floral diversity and prairie type (reconstructed or remnant), and landscape factors, in the form of surrounding agricultural production. We defined at-risk species in two ways. For bumble bees, we used the IUCN red list of bumble bees for North America. As other species in the bee community have not been systematically evaluated, we used an independent data set to calculate a community-level measure of rarity as a proxy for at-risk species. We calculated community rarity metrics using a Species Weighted Mean (SWM) approach, with species-level rarity (relative abundance and site occurrence) derived from a regional dataset comprised of over 30,000 specimens from across the US state of Minnesota. We found that the declining bumble bee <em>Bombus</em> <em>fervidus</em> had higher abundances in remnant rather than reconstructed prairies. Floral richness was associated with rarer bee communities (lower SWM values) across remnant and reconstructed prairies. We show that planting and managing prairies for floral diversity promotes bee communities with rarer species, but that remnants better support some at-risk species such as <em>Bombus</em> <em>fervidus</em>. </p>
Data and Code for: Resistance is futile: Weaker selection for resistance by abundant parasites increases prevalence and depresses host density
<p>We model host evolution of costly resistance to infection and its dependence on environmental factors, such as nutrients. We find that higher nutrients can increase infection prevalence AND select for lower resistance. In turn, the model predicts that lower resistance drives infection prevalence even higher while depressing host density. The attached code performs the model analysis, produces the published figures, and conducts statistical analysis on the data (described below). We conducted a mesocosm experiment with mixtures of zooplankton host (<em>Daphnia dentifera</em>) genotypes, algal resources (<em>Ankistrodesmus falcatus</em>), and fungal parasites (<em>Metschnikowia bicuspidata</em>). Mesocosm populations were supplied with low or high nutrients (5 or 50 ug/L phosphorus and 100 or 1000 ug/L nitrogen). We measured densities of hosts along with age class (juvenile or adult), sex, infections status, and egg number and chlorophyll densities; these data are a subset of data published previously Walsman et al. <em>Functional Ecology </em>(<a href="https://doi.org/10.1111/1365-2435.14030">https://doi.org/10.1111/1365-2435.14030</a>; data at <a href="https://doi.org/10.5061/dryad.mw6m905zg">https://doi.org/10.5061/dryad.mw6m905zg</a>). For the first time, we also report genotype frequencies for the mixed genotype treatments. Importantly, we found that high nutrients increased infection prevalence as well as selecting for the host genotype less resistant to infection; the resulting host evolution increased infection prevalence further and depressed host density. These data and code may be reused with citation of the corresponding publication ("'Resistance is futile': Weaker selection for resistance by abundant parasites increases prevalence and depresses host density" in <em>The American Naturalist</em>).</p>
Environmental data and fractional abundance of iso and branched GDGT data used to train the BIGMaC algorithm
<p>Location, environmental data -depth (m), elevation, distance to land (km), Mean Annual Air Temperature (C), and pH-, as well as fractional abundance of isoprenoid and branched GDGTs for unpublished samples used for the training of the Branched and Isoprenoid GDGT Machine learning Classification (BIGMaC) algorithm (Martínes-Sosa, et al., in prep).</p>
Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
<p><span>These data on bird species abundance and environmental variables were used in testing and comparing two different species distribution model validation methods that are applied to models which are used to predict the effects of climate change on species' distributions. The aim of the study was to investigate whether different validation methods give different results of the model's predictive performance and to demonstrate that validation methods based on measuring and validating a "static" pattern in distribution can assess model performance over-optimistically compared to methods based on measuring and validating a "change" in the distribution, which can assess the predictive performance more critically. </span></p>
Input data, species level results and code accompanying paper: Drivers of the changing abundance of European birds at two spatial scales
<p>This repository contains the input data, species level results and code associated with the paper: <strong>Drivers of the changing abundance of European birds at two spatial scales. </strong></p>
Data and code for: Changes in prey body size differentially reduces predation risk across predator and prey abundances
<p>Trophic interactions underpin the structure of ecological communities by describing the rate at which consumers exploit their resources. The rates at which predators consume their prey are influenced by prey traits, with many species inducing defensive modifications to prey traits following the threat of predation. Here we use different clonal lines of the protist <em>Paramecium</em> being consumed by <em>Stenostomum</em> predators to highlight how differences in prey traits impact rates of predation. Clonal lines differed in their body width traits and in their ability to induce changes in body width. By using a factorial cross of predator and prey abundances for different clonal lines we demonstrate how evolutionary or induced alterations in prey traits can impact the relative threat of predation. Our experiments show how interference among predators impacts predation rate and how increased body width increased predator handling times. Given that reductions in the strength of interspecific interactions are associated with increased levels of overall community stability, our results indicate how individual-level changes may scale up to impact whole communities. </p>
Data for: A model of ecological abundance: Terrestrial species inventories
<p>Counts of species in ecological samples are important for two reasons: they tell us about community assembly processes and they form the basis of species diversity estimates. Previous models of count distributions are either complex, widely rejected, not grounded in population dynamics, or not able to predict high unevenness. I present a new one-parameter model assuming that individual counts track the geometric series. The series' governing parameter <em>p</em> is set to vary randomly among species. Communities differ only in the centering of the distribution of <em>p</em>. To find the probability distribution, a vector of evenly-spaced initial values called q is drawn from the range 0 to 1. Values are then scaled by (1) transforming each q into the odds <em>o</em> = <em>q</em>/(<em>1 – q</em>), (2) multiplying each o by a fitted parameter <em>m</em>, and (3) back-computing each <em>p</em> as <em>m o</em>/(<em>m o </em>+ <em>1</em>). This skews the values to match the centering of the actual counts. The distribution is consistent with a population dynamics model in which the number of offspring produced in each interval by each species is distributed geometrically, rising with the number of adults. Large-scale surveys of corals, fishes, butterflies, and trees are consistent with the distribution, as are local-scale inventories of trees and assorted vertebrate and insect groups. Each local survey is used to predict counts within biogeographically and taxonomically matched surveys. When only decisive differences are considered, the model's predictions outperform those of each rival in at least 86% of all pairwise comparisons. The new distribution's estimates haves no substantial sample size bias. Thus, it is preferable to other species diversity estimation methods in the frequent cases where it is a good fit to count data.</p>
Data and code for the publication "Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil"
<p><strong>Background</strong></p> <p>The dataset contains data on soil properties and microplastic abundance in soil samples taken in Langel-Merkenich (Cologne, Germany). It was analysed in the paper by M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” <a href="https://doi.org/10.1016/j.scitotenv.2022.155141">https://doi.org/10.1016/j.scitotenv.2022.155141</a>) published in Science of the Total Environment.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The zipped folder</p> <ul> <li><strong>data.zip</strong> contains subfolders <strong>imagelab_data</strong> (contains microplastics data by depth and the blanks), <strong>additional_data</strong> (weights of aliquots and polymer densities) and <strong>soil_data</strong> (data on soil properties and images of soil profiles).</li> <li><strong>images.zip</strong> contains the workflow diagram, which is plotted in the R notebook Data_read_in.Rmd.</li> <li><strong>results.zip</strong> contains the results of reading and wrangling of microplastics data.</li> </ul> <p> </p> <p><strong>Description of the code</strong></p> <p>The R notebooks *.Rmd contain the code to read and wrangle the microplastics data (Data_read_in_corrected.Rmd), analyse and plot it (Analysis_plots_MP.Rmd) and plot data on soil properties (Plots_soil_data.Rmd).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p> </p> <p><strong>Funding</strong></p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.</p> <p> </p> <p><strong>References</strong></p> <p>M. Rolf, H. Laermanns, L. Kienzler, C. Pohl, J.N. Möller, C. Laforsch, M.G.J. Löder and C. Bogner, “Flooding frequency and floodplain topography determine abundance of microplastics in an alluvial Rhine soil” <a href="https://doi.org/10.1016/j.scitotenv.2022.155141">https://doi.org/10.1016/j.scitotenv.2022.155141</a></p> <p>R Core Team, 2021, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, https://www.R-project.org/</p>
Data and Analyses for Discoderus and overall Carabidae abundance for Arizona
<p>This dataset is derived from the NEON Ground Beetle pitfall trapping data product: https://data.neonscience.org/data-products/DP1.10022.001</p> <p>Data from the Santa Rita Experimental Range (SRER) from southern Arizona is analyzed to understand temporal patterns in abundance of the carabid genus Discoderus. This dataset is associated with a manuscript submitted to the Coleopterists Bulletin for publication.</p>
Extracting abundance information from DNA-based data
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Data from: Semi-natural habitat, but not aphid amount or continuity, predicts lady beetle abundance across agricultural landscapes
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Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
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Data from: Species that dominate spatial turnover can be of (almost) any abundance
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CABO forest inventory survey data: Canopy-level spectra, species abundances, and environmental conditions
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Data from: Fecal biomarkers in soils record landscape-scale wild herbivore abundance
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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)
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DANDI Archive for NWB datasets
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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.