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3,121 results for “Resources”
Seal framework resource
<p>Seal is a framework for training sequence-based deep learning models that predict variant effect on gene expression in specific biological contexts with limited data. This record contains the following resource files that are needed for running Seal framework code: hg19 genome FASTA files, gene annotation files, expression matrix files, pre-trained CNN model for chromatin profile prediction, pre-computed chromatin profiles of TSS-proximal regions.</p>
Salmonella In Silico Typing Resource (SISTR) commandline tool database version 1.1 used by SISTR tool up to release version 1.1.2 inclusive
<h3>Context</h3> <p>Salmonella In Silico Typing Resource (<a href="https://github.com/phac-nml/sistr_cmd/tree/master">SISTR</a>) commandline tool enables the identification of the Salmonella serovar and cgMLST types of <em>Salmonella</em> from whole genome sequencing (WGS) data tby using a large database (10,000+) of <em>Salmonella</em> genomes and cgMLST profiles based on the 330 alleles. This database is the central part of the SISTR tool and contains both metadata on 2672 serovars and the corresponding antigenic formula, 84464 genomes to serovar mappings, sequences of the O, H1 and H2 antigens, 139729 cgMLST sequences and 38240 profiles with pairwise distances, MASH sketch of the 15465 genomes used for species and serovar identification.</p> <p>For more information and citation please refer to the following publication and official repository at <a href="https://github.com/phac-nml/sistr_cmd/tree/master">https://github.com/phac-nml/sistr_cmd/tree/master </a></p> <p>Note: This database was used by SISTR tool up to version 1.1.2 inclusive. From SISTR release 1.1.3 onwards the slightly modified version of this database will be used onwards with changes detailed in <a href="https://github.com/phac-nml/sistr_cmd/blob/master/CHANGELOG.md">https://github.com/phac-nml/sistr_cmd/blob/master/CHANGELOG.md</a></p> <h3>Citation</h3> <p><em>The Salmonella In Silico Typing Resource (SISTR): an open web-accessible tool for rapidly typing and subtyping draft Salmonella genome assemblies. Catherine Yoshida, Peter Kruczkiewicz, Chad R. Laing, Erika J. Lingohr, Victor P.J. Gannon, John H.E. Nash, Eduardo N. Taboada. PLoS ONE 11(1): e0147101. doi: 10.1371/journal.pone.0147101. <a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101" rel="nofollow">http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147101</a></em></p> <p> </p> <p> </p>
Dataset for "Open Educational Resources for distributed hands-on teaching in molecular biology"
<p>This is the raw data for an article that describes novel OER consisting of low cost and locally produced public domain biological reagents, open source hardware and free software collaborative notebooks to teach LAMP DNA amplification, RT-PCR RNA detection, enzyme kinetics and fluorescence imaging. </p>
Mapping and Quantifying Mexico's Primary Forests: An Operational Approach for Forest Resources Assessment
<h2>Supplementary material of the paper</h2> <h3>Authors:</h3> <ul> <li>Luis Felipe Castelblanco Rivera, master student in Earth Science at Geosciences Center, UNAM.</li> <li>Mario Antonio Guevara Santamaría, associate professor at Geosciences Center, UNAM campus Juriquilla</li> </ul> <h3>More information:</h3> <ul> <li>lcastelblancor@geociencias.unam.mx / lfcastelblancor@unal.edu.co</li> <li>mguevara@geociencias.unam.mx / mguevara@comunidad.unam.mx</li> </ul> <p>Supplementary material containing the vector layers with the information on the exclusion areas and the forest resulting from the multicriteria analysis for the identification of primary forests in Mexico for the years 2015 and 2020.</p> <h3>Summary:</h3> <p>The file is organized as follows:</p> <p>> Primary forest MX</p> <p> > 2015</p> <p> > Forest areas</p> <p> - Forest_1_2 (Bosque_1_2.shp)</p> <p> - Forest_1_3_1_Infrastructure (Bosque_1_3_1_Infraestructura.shp)</p> <p> - Forest_1_3_2_Deforestation (Bosque_1_3_2_Deforestacion.shp)</p> <p> - Forest_1_4_45degrees (Bosque_1_4_45grados.shp)</p> <p> - Forest_1_4_3000masl (Bosque_1_4_3000msnm.shp)</p> <p> - Forest_1_5_NPA (Bosque_1_5_ANP.shp)</p> <p> - Forest_1_6_patch_size_2015 (Bosque_1_6_tamaño_parche_2015.shp)</p> <p> - Primary_forest_2015 (Bosque_primario_2015.shp)</p> <p> > Exclusion areas</p> <p> - Deforestation2015_1km (Deforestacion2015_1km.shp)</p> <p> - AgriculturalBorder_SII_1km (FronteraAgricola_SII_1km.shp)</p> <p> - Infrastructure_1km (Infraestructura_1km.shp)</p> <p> - redvial2015_1km (redvial2015_1km.shp)</p> <p> > 2020</p> <p> > Forest areas</p> <p> - Forest_1_2 (Bosque_1_2.shp)</p> <p> - Forest_1_3_1_Infrastructure (Bosque_1_3_1_Infraestructura.shp)</p> <p> - Forest_1_3_2_Deforestation (Bosque_1_3_2_Deforestacion.shp)</p> <p> - Forest_1_4_45degrees (Bosque_1_4_45grados.shp)</p> <p> - Forest_1_4_3000msnm (Bosque_1_4_3000msnm.shp)</p> <p> - Forest_1_5_NPA (Bosque_1_5_ANP.shp)</p> <p> - Forest_1_6_patch_size_2015 (Bosque_1_6_tamaño_parche_2015.shp)</p> <p> - Primary_forest_2020 (Bosque_primario_2020.shp)</p> <p> > Exclusion areas</p> <p> - Deforestation2020_1km (Deforestacion2020_1km.shp)</p> <p> - Agricultural_Front_SIII_1km (FronteraAgricola_SIII_1km.shp)</p> <p> - Infrastructure_1km (Infraestructura_1km.shp)</p> <p> - road_network2020_1km (redvial2020_1km.shp)</p> <p>*All shape files are accompanied by their respective auxiliary files.</p> <p>Supplementary material 1-4.docx: Word file containing the supplementary materials named in the article, in the following order:</p> <ul> <li>Supplementary material 1. Decision tree.</li> <li>Supplementary material 2. Data sets.</li> <li>Supplementary material 3. Primary forest area by ecoregion and forest type for 2015 and 2020</li> <li>Supplementary material 4. Statistics on concordance and ecological integrity values in primary and other forests in 2020.</li> </ul> <h2>Material suplementario del artículo </h2> <h3>Autores:</h3> <p>- Luis Felipe Castelblanco Rivera, master student in Earth Science at Geosciences Center, UNAM.</p> <p>- Mario Antonio Guevara Santamaría, associate professor at Geosciences Center, UNAM campus Juriquilla </p> <h3>Más información:</h3> <p>- lcastelblancor@geociencias.unam.mx / lfcastelblancor@unal.edu.co</p> <p>- mguevara@geociencias.unam.mx / mguevara@comunidad.unam.mx</p> <p><strong>Bosque primario MX.7z: </strong>Material suplementario que contiene las capas vectoriales con las información de las áreas de exclusión y el bosque resultante del análisis multicriterio para la identificación de bosques primarios en México para los años 2015 y 2020. </p> <h3>Resumen:</h3> <p>El archivo se encuentra organizado de la siguiente forma:</p> <p>> Bosque primario MX</p> <p> > 2015</p> <p> > Áreas de bosque</p> <p> - Bosque_1_2.shp</p> <p> - Bosque_1_3_1_Infraestructura.shp</p> <p> - Bosque_1_3_2_Deforestacion.shp</p> <p> - Bosque_1_4_45grados.shp</p> <p> - Bosque_1_4_3000msnm.shp</p> <p> - Bosque_1_5_ANP.shp</p> <p> - Bosque_1_6_tamaño_parche_2015.shp</p> <p> - Bosque_primario_2015.shp</p> <p> > Áreas de exclusión</p> <p> - Deforestacion2015_1km.shp</p> <p> - FronteraAgricola_SII_1km.shp</p> <p> - Infraestructura_1km.shp</p> <p> - redvial2015_1km.shp</p> <p> > 2020</p> <p> > Áreas de bosque</p> <p> - Bosque_1_2.shp</p> <p> - Bosque_1_3_1_Infraestructura.shp</p> <p> - Bosque_1_3_2_Deforestacion.shp</p> <p> - Bosque_1_4_45grados.shp</p> <p> - Bosque_1_4_3000msnm.shp</p> <p> - Bosque_1_5_ANP.shp</p> <p> - Bosque_1_6_tamaño_parche_2015.shp</p> <p> - Bosque_primario_2020.shp</p> <p> > Áreas de exclusión</p> <p> - Deforestacion2020_1km.shp</p> <p> - FronteraAgricola_SIII_1km.shp</p> <p> - Infraestructura_1km.shp</p> <p> - redvial2020_1km.shp</p> <p>*Todos los archivos shape estan acompomañados de sus respectivos archivos auxiliares.</p> <p>Supplementary material 1-4.docx: Archivo Word que contiene los materiales suplementarios nombrados en el articulo, en el siguiente orden:</p> <ul> <li>Material complementario 1. Árbol de decisiones.</li> <li>Material complementario 2. Conjuntos de datos.</li> <li>Material complementario 3. Área de bosque primario por ecorregión y tipo de bosque para 2015 y 2020</li> <li>Material complementario 4. Estadísticas sobre valores de concordancia e integridad ecológica en bosques primarios y otros bosques en 2020.</li> </ul>
Dataset for Validity and reliability study of Personal Resource Questionnaire-2000 Indonesia version (PRQ2000-INA) to measure perceived social support among people with dementia in Indonesia
<p>This is a dataset for "Validity and reliability study of Personal Resource Questionnaire-2000 Indonesia version (PRQ2000-INA) to measure perceived social support among people with dementia in Indonesia"</p>
Resources of "Evolving antibody response to SARS-CoV-2 antigenic shift from XBB to JN.1"
<p>Resources of the article "Evolving antibody response to SARS-CoV-2 antigenic shift from XBB to JN.1". See https://github.com/yunlongcaolab/SARS-CoV-2-JN.1-mAbs for future updates.</p>
Care to Share: Dataset and resources for Dutch National Open Science Festival hackathon
<p>This dataset contains the materials used in the session "Care to Share? Investigating Open Science practices adoption among researchers: a hackathon" presented at the Dutch National Open Science Festival on 22nd October 2024.</p> <p>The data files are derived from: Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 ad contains two additional fields (Dimensions_Country and Dimensions_FoR) from Dimensions obtained on 15 October 2024, from Digital Science’s Dimensions platform, available at https://app.dimensions.ai. </p> <h2>File list:</h2> <p><strong>PLOS-Dataset-for-Hackathon.xlsx</strong></p> <p>Data pertaining to the PLOS corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai. </p> <p><strong>Comparator-Dataset-for-Hackathon.xlsx</strong></p> <p>Data pertaining to the Comparator corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai.</p> <p><strong>Care to share resource sheet.pdf</strong></p> <p>Document outlining the questions to be investigated during the hackathon as well as key information about the dataset.</p> <p><strong>OSI-Column-Descriptions_v3_Dec23.pdf</strong><br>This file is taken from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686. It describes the fields used in the two data files with the exception of Dimensions_Country and Dimensions_FoR. Descriptions for these are listed in the README tabs of the data files.</p>
Resources for SCSES test data
<p>This dataset consists of some input files that are essential for running the SCSES package.</p>
Model input for initial submission of "A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)" to GMD
<p>This dataset contains the numerical model input files that were used to produce the model simulation presented in the initual submission "A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)" to Geoscientific Model Development.</p> <p>When using these files to run a model, most files should be placed inside a directory named INPUT/ that resides in the working directory where the model is being run. The following files should be at the top level in the working directory: data_table, diag_table, field_table, input.nml.</p> <p>For large files, a subset in time is provided for the first year of the simulation. This dataset does not include the ERA5 atmospheric data, which can be downloaded directly from https://doi.org/10.24381/cds.adbb2d47.</p> <p>Portions of the initial and boundary condition data were generated using E.U. Copernicus Marine Service Information:https://doi.org/10.48670/moi-00021. Refer to the manuscript for references to other data sources.</p> <p>Codes for generating regional MOM6 initial conditions, boundary conditions and other necessary model inputs as well as diagnostic scripts are maintained on the NOAA CEFI GitHub Repository: https://github.com/NOAA-GFDL/CEFI-regional-MOM6/. </p>
[Replication Package] Enhancing Code Generation for Low-Resource Languages: No Silver Bullet
<p>This repository contains scripts and results related to the work <em>"Enhancing Code Generation for Low-Resource Languages: No Silver Bullet".<br><br></em><strong>Link to the GitHub repository: <a href="https://github.com/Devy99/low-resource-study">https://github.com/Devy99/low-resource-study</a></strong><em><br></em></p>
Constraints on global mined geological resource production from limited regional water availability
<p>This depository provides the input dataset and codes/analytical procedures required to assess the sustainable capacity of mineral production considering local water resources as a constraint. The output dataset derived from the study are also provided.</p>
Data from: Resource-mediated indirect effects of grassland management on arthropod diversity
Intensive land use is a driving force for biodiversity decline in many ecosystems. In semi-natural grasslands, land-use activities such as mowing, grazing and fertilization affect the diversity of plants and arthropods, but the combined effects of different drivers and the chain of effects are largely unknown. In this study we used structural equation modelling to analyse how the arthropod communities in managed grasslands respond to land use and whether these responses are mediated through changes in resource diversity or resource quantity (biomass). Plants were considered resources for herbivores which themselves were considered resources for predators. Plant and arthropod (herbivores and predators) communities were sampled on 141 meadows, pastures and mown pastures within three regions in Germany in 2008 and 2009. Increasing land-use intensity generally increased plant biomass and decreased plant diversity, mainly through increasing fertilization. Herbivore diversity decreased together with plant diversity but showed no response to changes in plant biomass. Hence, landuse effects on herbivore diversity were mediated through resource diversity rather than quantity. Land-use effects on predator diversity were mediated by both herbivore diversity (resource diversity) and herbivore quantity (herbivore biomass), but indirect effects through resource quantity were stronger. Our findings highlight the importance of assessing both direct and indirect effects of land-use intensity and mode on different trophic levels. In addition to the overall effects, there were subtle differences between the different regions, pointing to the importance of regional land-use specificities. Our study underlines the commonly observed strong effect of grassland land use on biodiversity. It also highlights that mechanistic approaches help us to understand how different land-use modes affect biodiversity.
Data from: "Identification of SNP markers for the endangered Ugandan red colobus (Procolobus rufomitratus tephrosceles) using RAD sequencing" in Genomic Resources Notes accepted 1 December 2014 to 31 January 2015
Despite dramatic growth in the field of primate genomics over the past decade, studies of primate population and conservation genomics in the wild have been hampered due to the difficulties inherent in studying non-model organisms and endangered species, such as lack of a reference genome and current challenges in de novo primate genome assembly. Here, we used Restriction-site Associated DNA (RAD) sequencing to develop a population-based SNP panel for the Ugandan red colobus (P. rufomitratus tephrosceles), which is a highly threatened monkey due to habitat loss. We analyzed blood samples from 24 individuals from Kibale National Park (Uganda) using single-end RAD sequencing. We obtained 70,773,857 reads, of which 58,814,906 passed the filtering steps. Using the program STACKS v. 1.11 we identified 113,376 loci, of which 50,558 were polymorphic and had a mean observed heterozygosity of 0.25. These data will be used to study the effects of habitat fragmentation on genomic diversity, dispersal, and disease transmission in this species. Our approach provides a good example of the potential of RAD sequencing in studies of wild primate populations.
Data from: Is tropical montane forest heterogeneity promoted by a resource-driven feedback cycle? Evidence from nutrient relations, herbivory and litter decomposition along a topographical gradient
1. Ridges of tropical mountains often differ strikingly from neighbouring ravines in terms of forest structure, productivity, and species composition. This heterogeneity is poorly understood despite its critical role in biodiversity maintenance, carbon and nutrient budgets. 2. We examined measures of tree biomass and productivity, foliage and litter quality (nutrient concentrations, specific leaf mass, phenolics), herbivory and leaf litter decomposition in each six plots laid out in upper and lower slope position in a tropical montane moist forest in southeastern Ecuador. 3. Productivity, quality of foliage and litter and herbivory were significantly lower in upper slope position and closely correlated with soil nutrient concentrations and accumulated humus. The decomposition of upper slope leaf litter (decomposition rate k) was substantially lower than in litter from lower slope forest, whereas the site of decomposition (slope position) only had a marginal effect on the decomposition rate. 4. Our results suggest that the differences in stand structure, productivity, foliar quality, herbivory and decomposition between slope positions are ultimately due to stronger nutrient limitations in upper slope forest. We propose a general conceptual model that explains origin and maintenance of contrasting forest types along topographical gradients through down-slope fluxes of nutrients and water, and a nutrient-driven positive feedback cycle.
Data from: To kill a kangaroo: understanding the decision to pursue high-risk/high-gain resources
In this paper, we attempt to understand hunter–gatherer foraging decisions about prey that vary in both the mean and variance of energy return using an expected utility framework. We show that for skewed distributions of energetic returns, the standard linear variance discounting (LVD) model for risk-sensitive foraging can produce quite misleading results. In addition to creating difficulties for the LVD model, the skewed distributions characteristic of hunting returns create challenges for estimating probability distribution functions required for expected utility. We present a solution using a two-component finite mixture model for foraging returns. We then use detailed foraging returns data based on focal follows of individual hunters in Western Australia hunting for high-risk/high-gain (hill kangaroo) and relatively low-risk/low-gain (sand monitor) prey. Using probability densities for the two resources estimated from the mixture models, combined with theoretically sensible utility curves characterized by diminishing marginal utility for the highest returns, we find that the expected utility of the sand monitors greatly exceeds that of kangaroos despite the fact that the mean energy return for kangaroos is nearly twice as large as that for sand monitors. We conclude that the decision to hunt hill kangaroos does not arise simply as part of an energetic utility-maximization strategy and that additional social, political or symbolic benefits must accrue to hunters of this highly variable prey.
Data from: Isotopic and dietary niches as indicators for resource partitioning in the gleaner bats M. bechsteinii, M. nattereri, and P. auritus
The n-dimensional ecological niche summarizes all living requirements of a species. According to the competition and niche theory, co-occurring species have to differ in at least one dimension to live in stable coexistence. Measuring the overall ecological niche within natural ecosystems is probably impossible but methods that describe several dimensions at once like the isotopic niche are good approximations. However, the impact of each factor contributing to the isotopic niche might be difficult to estimate. The dietary niche, as one part of the isotopic niche, can be examined with high resolution using molecular techniques. In this study we aimed to improve our understanding of species coexistence. We outlined the importance of the isotopic and dietary niches in the context of resource partitioning using the bat species M. bechsteinii, M. nattereri, and P. auritus of the gleaner guild as examples. The dietary and isotopic niches were estimated with stable isotope analysis and molecular fecal analysis. We tested (i) how distinct the occupied dietary niches of members of the same guild are and (ii) how similar the dietary and isotopic niches are to each other. While the inter-specific overlap was high for the dietary niche, no overlap could be observed for the isotopic niche. In general, the isotopic niche can describe a more complete picture of the ecological niche, while the dietary niche provides highly detailed information. The combination of both niches might advance our understanding of stable species coexistences.
Data from: The evolution of competitive ability for essential resources
<p>Competition for limiting resources is among the most fundamental ecological interactions and has long been considered a key driver of species coexistence and biodiversity. Species' minimum resource requirements, their R*s, are key traits that link individual physiological demands to the outcome of competition. However, a major question remains unanswered - to what extent are species' competitive traits able to evolve in response to resource limitation? To address this knowledge gap, we performed an evolution experiment in which we exposed Chlamydomonas reinhardtii for approximately 285 generations to seven environments in chemostats which differed in resource supply ratios (including nitrogen, phosphorus and light limitation) and salt stress. We then grew the ancestors and descendants in common garden and quantified their competitive abilities for essential resources. We investigated constraints on trait evolution by testing whether changes in resource requirements for different resources were correlated. Competitive abilities for phosphorus improved in all populations, while competitive abilities for nitrogen and light increased in some populations and decreased in others. In contrast to the common assumption that there are trade-offs between competitive abilities for different resources, we found that improvements in competitive ability for a resource came at no detectable cost. Instead, improvements in competitive ability for multiple resources were either positively correlated or not significantly correlated. Using resource competition theory, we then demonstrated that rapid adaptation in competitive traits altered the predicted outcomes of competition. These results highlight the need to incorporate contemporary evolutionary change into predictions of competitive community dynamics over environmental gradients.</p>
Data from: Consumer versus resource control and the importance of habitat heterogeneity for estuarine bivalves
The relative influence of consumers (top down) and resources (bottom up) on the distribution and abundance of organisms remains a key question in ecology. We examined the relationships between consumer and resource variables along a productivity gradient for a dominant predator–prey interaction in a marine soft-sediment system. We 1) quantified density and size of the clam Macoma balthica (prey species) in six replicate sites at each of four habitat types (shallow mud, deep mud, muddy sand and detrital mud) in the Rhode River, Chesapeake Bay. We selected one habitat type of high food availability and clam density (shallow mud) and another of low food availability and clam density (muddy sand) for manipulative experiments. Then, we 2) measured M. balthica survival and growth through transplants, 3) measured food availability as sedimentary organic carbon content, 4) quantified predator density, and 5) calculated predator foraging efficiency in the two habitat types. Clam density in the four habitat types differed and was related to sedimentary carbon availability and predator density. One of the habitats, detrital mud, appeared to be a population sink because it only held juvenile Macoma that never survived to reproductive age. Macoma size and growth, and predator (mainly blue crab Callinectes sapidus) densities were positively correlated with productivity and were higher in shallow mud than muddy sand. In contrast, Macoma mortality, local 'interaction strength', and predator foraging efficiency were lower in the productive habitat (shallow mud). Thus, predation intensity was inversely correlated with productivity (food availability); consumer and resource effects differed by habitat type; and, at a relatively small spatial scale, consumer and resource forces jointly determined population dynamics in this soft-sediment marine system.
Data from: Local and landscape-level floral resources explain effects of wildflower strips on wild bees across four European countries
1. Growing evidence for declines in wild bees calls for the development and implementation of effective mitigation measures. Enhancing floral resources is a widely accepted measure for promoting bees in agricultural landscapes, but effectiveness varies considerably between landscapes and regions. We hypothesize that this variation is mainly driven by a combination of the direct effects of measures on local floral resources and the availability of floral resources in the surrounding landscape. 2. To test this, we established wildflower strips in four European countries, using the same seed mixture of forage plants specifically targeted at bees. We used a before–after control–impact approach to analyse the impacts of wildflower strips on bumblebees, solitary bees and Red List species and examined to what extent effects were affected by local and landscape-wide floral resource availability, land-use intensity and landscape complexity. 3. Wildflower strips generally enhanced local bee abundance and richness, including Red-listed species. Effectiveness of the wildflower strips increased with the local contrast in flower richness created by the strips and furthermore depended on the availability of floral resources in the surrounding landscape, with different patterns for solitary bees and bumblebees. Effects on solitary bees appeared to decrease with increasing amount of late-season alternative floral resources in the landscape, whereas effects on bumblebees increased with increasing early-season landscape-wide floral resource availability. 4. Synthesis and applications. Our study shows that the effects of wildflower strips on bees are largely driven by the extent to which local flower richness is increased. The effectiveness of this measure could therefore be enhanced by maximizing the number of bee forage species in seed mixtures, and by management regimes that effectively maintain flower richness in the strips through the years. In addition, for bumblebees specifically, our study highlights the importance of a continuous supply of food resources throughout the season. Measures that enhance early-season landscape-wide floral resource availability, such as the cultivation of oilseed rape, can benefit bumblebees by providing the essential resources for colony establishment and growth in spring. Further research is required to determine whether, and under what conditions, wildflower strips result in actual population-level effects.
Data from: Environmental resource deficit may drive the evolution of intraspecific trait variation in invasive plant populations
Intraspecific trait variation within natural populations (i.e. intra‐population trait variation, IPTV) is the basic source for selection and can have significant ecological consequences. Higher IPTV may increase a population's niche breath and benefit interspecies competition under a resource‐limited environment, thus affecting the ability of a species to move into novel habitats. However, the reciprocal influences of variation in environmental conditions and phenotypic trait expression in spreading plant populations are not clearly defined. We propose that during invasion, IPTV and its relative change in response to key resource enrichment may increase with the resource deficit of invaded sites, and that this relationship may facilitate plant invasions into resource‐limited environments. We analyzed the invasion trend, IPTV and its response to water enrichment, and moisture variability among populations of an annual grass Brachypodium hybridum in California, United States. We incorporated a genotyping‐by‐sequencing approach, a common garden experiment that had two water level treatments, and public plant and climate databases. Our hypothesis was supported by the observation that for populations that invaded sites with higher spring moisture deficit, both their seed biomass IPTV (for the water‐enriched treatment only) and relative change of the IPTV across water treatments were larger when examined in the common garden experiment. A generally north to south spreading direction was found in these B. hybridum populations, towards a drier and warmer climate exhibiting higher moisture deficit for plant growth. Our results suggest a role for interactions between IPTV (rather than trait means) and environmental resource availability in promoting plant invasions, providing new insights into the significance of IPTV in shaping plant geographic distributions.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.