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22,445 results for “diversity”
Geochemical, physicochemical, and genomic data from a continental-scale survey of microbial diversity in Antarctic soils (2003-2023)
This data package offers comprehensive insights into Antarctic soil microbial diversity and composition. From 2003 to 2023, a total of 186 samples were collected from diverse locations spanning the Antarctic Peninsula to East Antarctica, representing a wide range of environmental gradients and climatic conditions. Soils were stored at -20°C to preserve their integrity for downstream analyses. This data package integrates cultivation-independent sequencing of prokaryotic and fungal communities alongside a robust cultivation-dependent culture collection to enable direct comparisons across microbial diversity assessment methods. Accompanying geochemical, physicochemical, and environmental parameters provide critical context for biogeographical analyses, offering a valuable resource for studying microbial adaptations and community dynamics in extreme Antarctic environments.
MCR LTER: Coral Reef: Distinguishing the molecular diversity, nutrient content, and energetic potential of exometabolomes produced by macroalgae and reef-building corals; data for Kelly et al., 2022 PNAS
Metabolites exuded by primary producers comprise a significant fraction of marine dissolved organic matter, a poorly characterized, heterogenous mixture that dictates microbial metabolism and biogeochemical cycling. We present a foundational untargeted molecular analysis of exudates released by coral reef primary producers using liquid chromatography–tandem mass spectrometry to examine compounds produced by two coral species and three types of algae (macroalgae, turfing microalgae, and crustose coralline algae [CCA]) from Mo’orea, French Polynesia. Of 10,568 distinct ion features recovered from reef and mesocosm waters, 1,667 were exuded by producers; the majority (86%) were organism specific, reflecting a clear divide between coral and algal exometabolomes. These data allowed us to examine two tenets of coral reef ecology at the molecular level. First, stoichiometric analyses show a significantly reduced nominal carbon oxidation state of algal exometabolites than coral exometabolites, illustrating one ecological mechanism by which algal phase shifts engender fundamental changes in the biogeochemistry of reef biomes. Second, coral and algal exometabolomes were differentially enriched in organic macronutrients, revealing a mechanism for reef nutrient-recycling. Coral exometabolomes were enriched in diverse sources of nitrogen and phosphorus, including tyrosine derivatives, oleoyl-taurines, and acyl carnitines. Exometabolites of CCA and turf algae were significantly enriched in nitrogen with distinct signals from polyketide macrolactams and alkaloids, respectively. Macroalgal exometabolomes were dominated by nonnitrogenous compounds, including diverse prenol lipids and steroids. This study provides molecular-level insights into biogeochemical cycling on coral reefs and illustrates how changing benthic cover on reefs influences reef water chemistry with implications for microbial metabolism. This material is based upon work supported by the U.S. National Science Founda
Data from: Genetic admixture increases phenotypic diversity in the nectar yeast Metschnikowia reukaufii,
<p>Raw data and supplementary files for the manuscript "Genetic admixture increases phenotypic diversity in the nectar yeast <em>Metschnikowia reukaufii</em>."</p> <p>-------------------</p> <p><strong>Table S5.xlsx </strong>-- Pairwise correlations between phenotypic traits of <em>Metschnikowia reukaufii</em>.</p> <p><strong>Table S6.xlsx</strong> -- Detailed results obtained in tests of phylogenetic signal for different phenotypic traits and indices of overall performance of <em>Metschnikowia reukaufii</em>.</p> <p><strong>Table S7.xlsx</strong> -- Detailed model fitting results obtained for phenotypic traits and indices of overall performance of <em>Metschnikowia reukaufii</em>.</p> <p><strong>mronlyvcf-renamed.vcf</strong> -- High coverage SNPs obtained from whole genome mapping of 73 <em>Metschnikowia reukaufii</em> strains to diploid reference (mean coverage = 47.9×, range 23 – 116×).</p> <p><strong>MR_phenotypes.xlsx</strong> -- Phenotypic data obtained for 73 <em>Metschnikowia reukaufii</em> strains.</p>
Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures
<p>To whom concerned,</p> <p>This dataset is the supplementary dataset for the publication in <em>Environmental Research Letters</em> entitled '<a href="https://dx.doi.org/10.1088/1748-9326/abc122"><em>Mangrove diversity loss under sea-level rise triggered by bio-morphodynamic feedbacks and anthropogenic pressures</em></a>' authored by Danghan Xie, et al. in 2020. The publication can be freely downloaded here: <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abc122">https://iopscience.iop.org/article/10.1088/1748-9326/abc122</a>. The dataset consists of both model results and corresponding codes that one can easily reproduce figures either in the manuscript or the supplementary document. </p> <p>To use the code, one needs to pre-install the Matlab (R2017a) and changes the pre-set route (in the code) to the directory where the dataset is stored. The figure shapes may vary with the size of the user's monitor so output figures may be either squeezed or extended in unpredictable ways, but the window size of the figure can be adjusted to match the shape and the results will not be affected.</p> <p>The author is appreciated that any potential concerns or questions regarding our research from any party or person, so please contact me through the email: <a href="mailto:d.xie@uu.nl">d.xie@uu.nl</a> or <a href="mailto:xiedanghan@gmail.com">xiedanghan@gmail.com</a>. To know more about my research, you can also follow the <a href="https://www.researchgate.net/profile/Danghan_Xie">ResearchGate</a>.</p> <p>With Kind Regards,</p> <p>Danghan</p> <p>11th of November, 2020</p>
Data and code from: Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community
<p>To study changes in flying insect communities, and hoverflies in particular, malaise trap samples from a German site were compared between two years (Hallmann et al. 2020). The data files deposited here contain data obtained from six malaise traps in the Wahnbachtal (North Rhine-Westphalia, Germany, 50.851944N, 7.320833E) that were deployed in 1989 and again in 2014, at the exact same locations. Traps were situated in wet meadows as well as tall perennial meadows, in close proximity to shrub corridors, to forest–grassland borders, and to the Wahnbach River and surrounded by agricultural land, essentially a rather heterogeneous habitat. The Wahnbach River and the greater part of the valley are protected for watershed purposes and are subject to nature conservation management by the Wahnbach Talperrenverband. Hence, several restrictions apply to safeguard against water contamination.</p> <p>Total insect biomass collected with these traps was already included in Hallmann et al. (2017), but here we focus on additional information: the abundance and richness of hoverflies (Syrphidae) in each of the collected samples (pots). Methodologies of collection are described in Sorg (1990), Schwan et al. (1993), Sorg et al. (2013), Hallmann et al. (2017), and Ssymank et al. (2018). In brief, malaise traps were deployed throughout the growing season and operated continuously (day and night). Malaise trap construction (e.g., size, material, colouring, and ground sealing) and placing (e.g., positioning, orientation, and slope of the locations) were standardised in all aspects. Insect samples were preserved in 80% ethanol solution. Catches of the six traps investigated in the present study were emptied regularly: On average exposure intervals were 7.0 d (SD = 0.5) in 1989 and 16.7 d (SD = 5.6) in 2014. Across the six traps in 2014 the total exposure time (in number of days) was 42% higher compared to 1989. All collected samples (n = 196) were used in the present analysis with in total 19,604 individual hoverflies counted, distributed over 162 species and 59 genera.</p> <p>To assess how environmental conditions have changed over the 25 year, several additional datasets were assembled. Climatic<br> data were obtained from 169 climatic stations and were used to interpolate daily weather variables to each trap location, using spatiotemporal kriging. These steps are described in detail in Hallmann et al. (2017).</p> <p>Our analysis (see R code) consists of three components. First, we considered total abundance, species richness, and species diversity, at two temporal scales: pooled per year, i.e., across the sampling season, and seasonally (i.e., per day), and we compared these metrics between 1989 and 2014. Second, we examined how total flying biomass (i.e., the weight of all trapped insects, of which hoverflies are only a small proportion) related to total abundance as well as species richness of hoverflies. Third, we derived persistence probabilities and population growth rate trends per species, to examine interspecific variation in these parameters.</p> <p>Descriptions of the deposited files:</p> <p><strong>Groups.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> Nspec = number of different hoverfly species found in a pot<br> Nind = number of hoverfly individuals found in a pot</p> <p><strong>Counts.csv</strong><br> A matrix of counts of individual hoverflies per pot per species. The 196 rows represent the pots in the same order as in the file 'Groups.csv'. The columns represent the 162 different hoverfly species found. The scientific species names are indicated in the column headers.</p> <p><strong>PairedData.csv</strong><br> pot = sample identifier<br> JAHR = year of sampling<br> MF_NR = identifier of each of the six malaise trap locations<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> NI = number of hoverfly individuals found in a potbiomass.daily<br> NSP = number of different hoverfly species found in a pot<br> biomass.daily = daily fresh weight [gram] of flying insects: total fresh weight in a pot divided by the number of sampling days.</p> <p><strong>ModelFrame.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> plot = identifier of each of the six malaise trap locations<br> date = date for which the weather variables are interpolated<br> daynr = day-of-the-year for which the weather variables are interpolated<br> altitude = altitude [m] of the malaise trap locations<br> year = year of sampling<br> temperature = interpolated temperature [degrees Celsius]<br> precipitation = interpolated precipitation [mm per day]<br> wind.speed = interpolated wind speed [m/s]</p> <p><strong>Data_Rcode.pdf</strong><br> This pdf provides the R-code behind the analysis of the Hoverfly data. Three datasets are provided along with this R-code document, namely "Counts.csv", "Groups.csv", "PairedData.csv" and "ModelFrame.csv". Additionally, the BUGS-code ""syrphidModel.jag" is required for running the daily-activity model in JAGS.</p> <p><strong>syrphidModel.jag</strong><br> This BUGS-code is required for running the daily-activity model in JAGS.</p>
Patterns and drivers of species diversity in the Indo-Pacific red seaweed Portieria: phylogenetic data
<p>Alignments, trees and Biogeobears analyses related to the study: Leliaert F, Payo DA, Gurgel CFD, Schils T, Draisma SGA, Saunders GW, Kamiya M, Sherwood AR, Lin S-M, Huisman John M, Le Gall L, Anderson RJ, Bolton John J, Mattio L, Zubia M, Spokes T, Vieira C, Payri CE, Coppejans E, D'hondt S, Verbruggen H, De Clerck O. Patterns and drivers of species diversity in the Indo-Pacific red seaweed Portieria. Journal of Biogeography. 2018;45(10):2299-313. doi:10.1111/jbi.13410</p> <p>Abstract: Biogeographical processes underlying Indo-Pacific biodiversity patterns have been relatively well studied in marine shallow water invertebrates and fishes, but have been explored much less extensively in seaweeds, despite these organisms often displaying markedly different patterns. Using the marine red alga Portieria as a model, we aim to gain understanding of the evolutionary processes generating seaweed biogeographical patterns. Our results will be evaluated and compared with known patterns and processes in animals. Species diversity estimates were inferred using DNA-based species delimitation methods. Historical biogeographical patterns were inferred based on a six-gene time-calibrated phylogeny, distribution data of 802 specimens, and probabilistic modelling of geographic range evolution. The importance of geographic isolation for speciation was further evaluated by population genetic analyses at the intraspecific level. We delimited 92 candidate species, most with restricted distributions, suggesting low dispersal capacity. Highest species diversity was found in the Indo-Malay Archipelago (IMA). Our phylogeny indicates that Portieria originated during the late Cretaceous in the area that is now the Central Indo-Pacific. The biogeographical history of Portieria includes repeated dispersal events to peripheral regions, followed by long-term persistence and diversification of lineages within those regions, and limited dispersal back to the IMA. Our results suggest that the long geological history of the IMA played an important role in shaping Portieria diversity. High species richness in the IMA resulted from a combination of speciation at small spatial scales, possibly as a result of increased regional habitat diversity from the Eocene onwards, and species accumulation via dispersal and/or island integration through tectonic movement. Our results are consistent with the biodiversity feedback model, in which biodiversity hotspots act as both ‘centres of origin’ and ‘centres of accumulation’, and corroborate previous findings for invertebrates and fish that there is no single unifying model explaining the biological diversity within the IMA.</p>
WEST Diversity Panel GBS information (Ferguson et al., 2020)
<p>Genotyping by sequencing information (5,512,653 SNPs on 850 individuals [842 unique]) used in the manuscript "Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions" (Ferguson et al., 2020) DOI:10.1101/2020.11.02.365213</p> <p>The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-DE-AR0000661. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.</p>
Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region"
<p>Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region". The study is published as open access and can be found at the following link: <a href="https://www.sciencedirect.com/science/article/pii/S2950289625000326">https://www.sciencedirect.com/science/article/pii/S2950289625000326</a></p> <p> </p> <p>The file "SWAT_USERSOIL.csv" was included to facilitate the assimilation of the soil mapping data into the Soil & Water Assessment Tool (SWAT, https://swat.tamu.edu/) for hydrological modeling. </p> <p> </p> <p>Regarding the raster files, please note:</p> <p>a) All values in these datasets have been multiplied by 10,000 to optimize file sizes.</p> <p>b) Files are named using the variable acronym, followed by the corresponding soil layer. For outputs derived from pedotransfer functions (PTFs), the PTF reference is appended after the variable acronym.</p> <p>c) Available data decrease with increasing soil layer number. This occurs because not all locations (grid cells) have the same soil depth or number of soil layers.</p> <p> </p> <p>If you have any questions about the dataset or its use, please don't hesitate to contact us.</p> <p> </p> <p> </p>
Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories
<p>These files provide useful data and supplementary material associated with the publication 'Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories' (MNT information, atmospheric forcings, R scripts used to process and draw the graphs from the EcH2O-iso simulations, and observed water discharges).</p>
Accompanying dataset; 'Agroforestry enhances biological activity, diversity and soil-based ecosystem functions in mountain agroecosystems of Latin America: A meta-analysis.'
<p>The database created as part of the meta-analysis is designed to facilitate the comparison of biological activity, diversity (BIAD), and ecosystem functions (EFs) between agroforestry systems (AFS) and other land-use types. It incorporates data extracted from selected studies, each record comprising a mean value, sample size, and a variance measure to compute standard deviation. The database also categorizes data according to 22 explanatory variables, including geographical coordinates, climate classification, soil type, AFS classification, and more, to characterize the sites and management systems involved. This detailed classification enables a nuanced analysis of how different factors might influence the BIAD and EFs in the context of AFS. The database supports the meta-analysis by allowing for the estimation of effect sizes using response ratios, which compare the relative difference in BIAD and EFs between AFS and other land uses. Data extraction from primary studies was meticulous, employing both direct and indirect methods such as graph digitizing software, and missing data were supplemented using reliable sources or direct communication with the original study authors. The comprehensive nature of this database ensures that the analysis can account for a wide range of variables that may affect the outcomes of interest in the meta-analysis. </p><p>For an in-depth exploration of the study's findings and methodology, refer to the comprehensive meta-analysis available in Global Change Biology (2024), entitled "<i>Agroforestry Enhances Biological Activity, Diversity, and Soil-Based Ecosystem Functions in Mountain Agroecosystems of Latin America: A Meta-Analysis</i>."</p>
Data for: Diversity of Functional Edaphic Macrofauna in Musa acuminata x Musa balbisiana (AAB) Agroecosystems
<p>The Dataset is linked to the article <strong>Diversity of Functional Edaphic Macrofauna in <em>Musa acuminata x Musa balbisiana</em> (AAB) Agroecosystems.</strong> The collected individuals were analyzed by order, and family and quantified and identified by gender (Database (Oxford). 2020: baaa062. PubMed: 32761142 PMC: PMC7408187.), The collection and taxonomic identification phase is explained in the protocol.i (dx.doi.org/10.17504/protocols.io.rm7vzby75vx1/v1).</p> <p>This dataset was a modification as was indicated for the #GlobalSoilMacroFauna | Official template to report Data to the MACROFAUNA database (<a href="../records/7691884">#GlobalSoilMacroFauna | Official template to report Data to the MACROFAUNA database (zenodo.org)</a>) cited by [Mathieu, J., Antunes, A. C., Barot, S., Bonato Asato, A. E. ., Bartz, M. L. C. ., Brown, G. G., Calderon-Sanou, I., Decaëns, T., Fonte, S. J., Ganault, P., Gauzens, B., Gongalsky, K. B., Guerra, C. A., Hengl, T., Lavelle, P., Marichal, R., Mehring, H., Peña-Venegas, C. P., Castro, D., Potapov, A., Thébault, E., Thuiller, W., Witjes, M., Zhang, C., & Eisenhauer, N. (2022). sOilFauna - a global synthesis effort on the drivers of soil macrofauna communities and functioning: WORKSHOP REPORT . <em>SOIL ORGANISMS</em>, <em>94</em>(2), 111–126. https://doi.org/10.25674/so94iss2id282]</p>
Diverse baleen whale acoustic occurrence around two sub-Antarctic Islands: A tale of residents and visitors
<p>This dataset contains the acoustic .wav file of all exemplar calls illustrated by the spectrograms in the manuscript figure, MS Excel Spreadsheet file with baleen whale call occurrence and environmental data, and the R code used for fitting the RF models. R codes must be run in the following manner:</p> <p>1. 01_tune_occ_enviro_rf_model_balance_baleen_whales</p> <p>2. 02_process_occ_enviro_rf_model_balance_baleen_whales</p> <p>The codes are self-explanatory given the comments contained therein, and the source code for fitting the codes is provided as 000_source_all.</p>
Plant diversity and indicator values within 200m radius of the Landklif plots
<p><span>Species numbers of vascular plants as assessed in vegetation surveys inside and within 200m radius of the Landklif plots. Vegetation inside the plots was sampled between mid-May and end of July 2019 (seven subplots, 10m2 sampling area per plot). Cover values for each species were estimated following the Braun-Blanquet scale. Species pools within 200m radius around the plot were assessed between mid-May and begin of August 2020 by standardized transect walks (walking time proportional to area percentages of dominant habitat types within 200m radius, 60 minutes total walking time in each circle). The dataset contains average species numbers of the subplots, total species numbers of the plots, and total species numbers within 200m radius of the plots, as well as mean Ellenberg indicator values on plot and 200m scale.</span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p> <p> </p>
Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020
<p>The presented human thermal bioclimate dataset was created in the frame of the <a href="https://theheatalarm.wordpress.com/">HEAT-ALARM</a> ("Development of a heat-health warning system in Greece") research project.</p> <p><strong>Initially developed for Greece</strong>, it consists of hourly values of population-weighted mPET (modified physiologically equivalent temperature), simulated by the RayMan Pro model for the period 1991-2020 and for 10 population subsets in 72 regional units and combinations thereof, which are based on the NUTS-3 (Nomenclature of Territorial Units for Statistics-3) classification in Greece, using the Copernicus European Regional Reanalysis (CERRA) at 5.5 km spatial resolution. The dataset also includes the main environmental drivers of mPET (e.g. temperature) at the same spatiotemporal resolution.</p> <p>In the framework of <strong>replicating</strong> the original dataset, the current version includes population-weighted values of mPET and its environmental drivers for six populations in five districts of <strong>Cyprus</strong> at the LAU-1 (Local Administrative Units-1) level, covering the period from 1991 to 2020. </p> <p>The code used to produce the presented data is available at: <a href="https://doi.org/10.5281/zenodo.10793067">https://doi.org/10.5281/zenodo.10793067</a>. It can be used to replicate the dataset not only directly in Greece, but also in any other country included in the CERRA domain after appropriate adjustments, as in the case of Cyprus above.</p> <p><em>Compared to the previous version of the dataset for Greece, this version includes vapor pressure (VP) instead of relative humidity (see README.txt for more details), as VP is more relevant for human-biometerological and health-related studies.</em><em> </em></p> <p><strong>References</strong></p> <p>Giannaros, C., Agathangelidis, I., Galanaki, E. <em>et al.</em> Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece. <em>Sci Data</em> <strong>11</strong>, 76 (2024). <a href="https://doi.org/10.1038/s41597-024-02923-y">https://doi.org/10.1038/s41597-024-02923-y</a> </p>
Species diversity and extinction risk of vertebrate pollinators in India
<p>This repository includes the data compiled and used for the study of <strong>‘Species diversity and extinction risk of vertebrate</strong><br><strong>pollinators in India’</strong>. If you use these data, please cite them along with our manuscript:</p> <blockquote> <p>Kallivalappil R., Grattarola F., de Alwis Pitts D., Cotter S.C. & Pincheira-Donoso D. (2024). Species diversity and extinction risk of vertebrate<br>pollinators in India. <em>Biodiversity and Conservation</em>. https://doi.org/10.1007/s10531-024-02848-3</p> </blockquote> <p> </p> <h2>Abstract</h2> <p>Animal pollinators underpin the functioning and persistence of ecosystems globally. However, the vital role of pollination is being progressively eroded by the worldwide decline of pollinator species caused by human-induced environmental degradation, resulting in rising costs to biodiversity, agriculture, and economy. Most studies quantifying pollinator diversity and declines have focused on insects, whereas vertebrate pollinators remain comparatively neglected. Here, we<br>present the first comprehensive study quantifying the macroecological patterns of species richness and extinction risk of bird and mammal pollinators in India, a region of extremely high biodiversity and increasing anthropogenic pressure. Our results reveal that hotspots of mammal pollinator diversity are restricted to the south of the Western Ghats, whereas bird pollinator diversity hotspots are scattered throughout the country. Analyses of hotspots of threatened species<br>(based on the IUCN Red List) show that only mammal pollinators are currently classified as threatened in India, whereas multiple hotspots of population declines were observed for birds, and primarily in the Southwest for mammal pollinators. Our analyses failed to identify a role for species traits as drivers of these patterns, whereas most pollinators appear to be threatened by agriculture, logging and hunting for food, and medicinal purposes. Pollinator endangerment has widescale<br>ecological and economic implications such as reduced food production, plant extinction, loss of functional and genetic diversity, and economic damage. We suggest protection of vertebrate pollinators should be emphasised in active conservation agendas in India.</p> <p> </p> <h2>Files</h2> <h3>Spatial</h3> <ul> <li><code>india.gpkg</code></li> <li><code>birds.gpkg</code></li> <li><code>mammals.gpkg</code></li> <li><code>how_to_read_gpkg_data.R</code></li> </ul> <h3>Phylogenetic</h3> <ul> <li><code>PGLS_phylogeny_birds.nex</code></li> <li><code>PGLS_phylogeny_mammals.nex</code></li> </ul> <h3>Tables</h3> <ul> <li><code>all_bird_traits.csv</code></li> <li><code>all_mammals_traits.csv</code></li> <li><code>threatened_mammals_traits.csv</code></li> <li><code>plant_pollinator_dataset.csv</code></li> <li><code>pollinator_plant_dataset.csv</code></li> <li><code>references.txt</code></li> </ul>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort
<p># Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort" - Rev #1</p> <p>This is the dataset for the paper titled "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort".</p> <p>In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised*</p> <p>## Survey 2019 ##<br> The raw survey data for the initial 2019 survey is available in the file *survey2019_anon.csv*. Note that the data is anonymised as free-text comments have been removed. Explanations on the variables and their levels are given in the files *variables_survey2019.csv* and *values_survey2019.csv*.<br> The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*.</p> <p>## Survey 2020 ##<br> The raw survey data for the 2020 survey is available in the file *rdata_anon_survey2020.csv*. Additional scripts are supplied to reproduce the exploratory factor analysis. The main entry is the file *EFA.R*, which imports the data. The file contains some comments on the process.<br> The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*.</p> <p>## Interviews ##<br> The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.</p>
Exploring the Pocillopora cryptic diversity: a new genetic lineage in the western Indian Ocean or remnants from an ancient one?
<p>Cryptic species and lineages have been widely reported during the last decades, particularly in the marine realm. Misidentifications and ignoring species complexes imply many consequences, notably biasing biodiversity and connectivity assessments, which in turn mislead our understanding of ecosystems and impact the effective design and management of conservation plans. Focusing on the Indo-Pacific coral genus <em>Pocillopora</em>, playing key roles in reef ecosystems as one of the main bio-constructors, we report the first <em>Pocillopora</em> PSH16 (ORF53; <em>sensu</em> Gélin et al. 2017, Mol Phylogenet Evol 109:430–446) colonies (<em>N</em> = 19) in the western Indian Ocean (Nosy Tanikely, Madagascar), 6,000 km further from its current distribution. Colonies were identified according to their mitochondrial Open Reading Frame (ORF) haplotype and Bayesian assignment tests based on 13-microsatellite genotypes. Additionally, we performed genetic structure and diversity analyses with sympatric colonies from other <em>Pocillopora</em> species and <em>Pocillopora</em> PSH16 colonies from the tropical southwestern Pacific, revealing (1) a weak clonal richness, (2) a weak genetic diversity and (3) a relative isolation for the newly reported PSH16 colonies. These colonies thus represent either a new, distinct and uncommon, genetic lineage, or isolated remnants of a wider one. In any case, unless specific management measures are implemented, their long-term maintenance seems compromised due to restricted gene flow within a restricted pool of genes.</p> <p> </p> <p>This dataset contains the microsatellite genotypes analysed (98 <em>Pocillopora</em> colonies × 13 loci + ORF). Missing data are encoded as "?". The sampling marine province and the population are indicated for each individual.</p>
From the Horse's Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents
<p>Word frequency pairs for courses A, B, C from: </p> <p>Brett A. Becker, Daniel Gallagher, Paul Denny, James Prather, Colleen Gostomski, Kelli Norris, and Garrett Powell. 2022. From the Horse’s Mouth: The Words We Use to Teach Diverse Student Groups Across Three Continents. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022), March 3–5, 2022, Providence, RI, USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3478431.3499392</p> <p><strong>When referring to this dataset, please cite the above article. That contains the DOI of this dataset. Please do not cite this dataset directly without citing the article.</strong></p>
Diel vertical migration promotes prokaryotic diversity in the Red Sea mesopelagic
<p>ABSTRACT: The diel vertical migration (DVM) of fish provides an active transport of labile dissolved organic matter (DOM) to the deep ocean, fueling the metabolism of heterotrophic bacteria and archaea. We studied the impact of DVM on the mesopelagic prokaryotic diversity of the Red Sea focusing on the mesopelagic deep scattering layer (DSL) between 450-600 m. Despite the general consensus of homogeneous conditions in the twilight zone, we observed variability in physico-chemical variables and distinct seasonal indicator prokaryotes inhabiting the DSL, representing between 2% (summer) to over 10% (winter) of total sequences. The DSL samples diverged from the surrounding mesopelagic waters in multidimensional scaling analysis and were distributed according to depth (47% of variance explained). We identified the sources of diversity that contribute to the DSL using spring depth profiles. On average, 7% was related to probable sinking from the epipelagic, 34% was common among the other mesopelagic waters and 38% was attributable to the DVM, with 21% of species being unique to the DSL. We conclude that the mesopelagic physico-chemical properties shape a rather uniform prokaryotic community, but that the 200 m wide DSL contributes uniquely and in a high proportion to the diversity of the Red Sea mesopelagic.</p> <p>The raw 16S sequences used in this research article are available at <a href="https://www.ebi.ac.uk/ena/browser/view/PRJEB49545">https://www.ebi.ac.uk/ena/browser/view/PRJEB49545</a> as 67 paired fastq sequences with consecutive accession numbers: ERX7411972 – ERX7412038.</p> <p>The 2 files stored in this repository represent: a) the clean 16S sequences count and taxonomic affiliation (SILVA132 Database) and b) the metadata associated to each of the 67 samples (lat, long, temperature, salinity, nutrient concentrations, bacterial abundance, bacterial size, etc)</p>
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