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2,399 results for “fragmentation”
Data from: Vrba was right: Historical climatic fragmentation, and not current climate, explains mammal biogeography
<p>Climate plays a crucial role in shaping species distribution and evolution over time. Dr. Elisabeth Vrba's Resource-Use hypothesis posited that zones at the extremes of temperature and precipitation conditions should host a greater number of climate specialist species than other zones because of higher historical fragmentation. Here, we tested this hypothesis by examining climate-induced fragmentation over the past 5 million years. Our findings revealed that, as stated by Vrba, the number of climate specialist species increases with historical regional climate fragmentation, whereas climate generalist species richness decreases. This relationship is approximately 40% stronger than the correlation between current climate and species richness for climate specialist species and 77% stronger for generalist species. These evidences suggest that the effect of climate historical fragmentation is more significant than that of current climate conditions in explaining mammal biogeography. These results provide empirical support for the role of historical climate fragmentation and physiography in shaping the distribution and evolution of life on Earth.</p>
Data from: Thermal habitat fragmentation in stratified lakes induces resource waves that brook charr track across seasons
<p>The spatial configuration of thermal habitats constrains the thermoregulatory performance of ectotherms. Thermal landscapes also vary through time, which is particularly relevant in seasonal environments such as temperate lakes. Indeed, elevated temperatures in the epilimnion of dimictic lakes during summer could substantially reduce the use of this habitat by cold-stenothermic fish during the stratified period. The main objective of this study was to evaluate whether thermal habitat fragmentation in stratified lakes modulates accessibility to resources that brook charr, <em>Salvelinus fontinalis</em>, which is a mobile consumer, can track across seasons. More specifically, we hypothesize that reduced access to the littoral habitat during summer enhances foraging opportunities in this habitat during winter. We used an automatic acoustic telemetry system offering full coverage of the lake to continuously record brook charr locations across seasons, and we estimated zoobenthos abundances in the littoral habitat using image processing and semi-automatic classification. While brook charr concentrate in the metalimnion of the pelagic habitat in summer, most individuals in winter shift to a shallow bay that is unexploited in summer due to thermal constraints. In this habitat, zoobenthos abundance is more than twice as high at the end of the summer compared to littoral habitats close to the thermal refuge in the pelagic habitat. Surprisingly, brook charr showed strong within-lake site fidelity between two consecutive summers, which suggests that spatial memory could be a key driver of seasonal habitat use in this lacustrine population. Overall, our results suggest that thermal barriers create fragmentation between littoral and pelagic habitats that in turn produces resource opportunities that brook charr can track across seasons.</p>
Data and code from: Neighborhood habitat gains increase plant species richness in forest fragments - Rosenblad & Sullivan (2024)
<p>This repository contains all data and R code necessary to reproduce the results of Rosenblad & Sullivan (2024) <span>Neighborhood habitat gains increase plant species richness in forest fragments. README.md explains how the files fit together.</span></p>
Experimental evaluation of genetic variability based on DNA metabarcoding from the aquatic environment: Insights from the Leray COI fragment
<p>Intraspecific genetic variation is important for the assessment of organisms' resistance to changing environments and anthropogenic pressures. Aquatic DNA metabarcoding provides a non-invasive method in biodiversity research, including investigations at the within-species level. Through the analysis of eDNA samples collected from the Peter the Great Gulf of the Japan Sea, in this study we aimed to evaluate the identification of Amplicon Sequence Variants (ASVs) in marine eDNA among abundant species of the <em>Zostera</em> sp. community: <em>Hexagrammos octogrammus</em>, <em>Pholidapus dybowskii</em> (Teleostei: Perciformes), and <em>Pandalus latirostris</em> (Arthropoda: Decapoda). These species were collected from two distant locations to produce mock communities and gather aquatic eDNA both on the community and individual level. Our approach highlights the efficacy of eDNA metabarcoding in capturing haplotypic diversity and the potential for this methodology to track genetic diversity accurately, contributing to conservation efforts and ecosystem management. Additionally, our results elucidate the impact of nuclear mitochondrial DNA segments (NUMTs) on the reliability of metabarcoding data, indicating the necessity for cautious interpretation of such data in ecological studies. Moreover, we analyzed 83 publicly available <em>COI</em> sequence datasets from common groups of multicellular organisms (Mollusca, Echinodermata, Crustacea, Polychaeta, and Actinopterygii). The results reflect the decrease in population diversity that arises from using the metabarcode compared to the <em>COI</em> barcode.</p>
Occupancy of two Colombian endemic birds (Habia gutturalis) and White-Mantled Barbet (Capito hypoleucus) in fragmented forests of the Central Andes in Colombia
<p>The Sooty Ant-Tanager (<em>Habia gutturalis</em>) and White-mantled Barbet (<em>Capito hypoleucus</em>) are endangered and endemic birds of Colombia. Both species have small geographic ranges and presumably low population sizes possibly due to habitat destruction and fragmentation. In order to estimate the effects of landscape features on the occupancy of both species, we sampled a variety of landscape configurations within the buffer zones of two hydroelectric impoundments in the Central Andes of Colombia and applied occupancy models to estimate the proportion of area occupied as a function of these covariates. We surveyed 35 point-counts in each hydroelectric impoundment, between June and July of 2014 and 2015. We used single-season models to estimate occupancy while recognizing imperfect detection. Mean occupancy estimates in the study area were similar for both species (0.61 SD=0.33 for the Sooty Ant-Tanager and 0.63 SD=0.25 for the White-mantled). Nonetheless, occupancy probability within the study area was very different between them. The best model for the Sooty Ant-Tanager indicated a decrease in occupancy with elevation, whereas the top model for the White-mantled Barbet indicated an increase in occupancy with distance from streams. Detection probabilities were similar for both species (>0.4) and declined significantly during the second year. Our results provide quantitative guidelines that can be used to evaluate and monitor the state of these populations on the short and long term.</p>
Data from: Affordable de novo generation of fish mitogenomes using amplification-free enrichment of mitochondrial DNA and deep sequencing of long fragments
<p>Biomonitoring surveys from environmental DNA make use of metabarcoding tools to describe the community composition. These studies match their sequencing results against public genomic databases to identify the species. However, mitochondrial genomic reference data are yet incomplete, only a few genes may be available, or the suitability of existing sequence data is suboptimal for species-level resolution. Here we present a dedicated and cost-effective workflow with no DNA amplification for generating complete fish mitogenomes for the purpose of strengthening fish mitochondrial databases. Two different long-fragment sequencing approaches using Oxford Nanopore sequencing coupled with mitochondrial DNA enrichment were used. One where the enrichment is achieved by preferential isolation of mitochondria followed by DNA extraction and nuclear DNA depletion ('mitoenrichment'). A second enrichment approach takes advantage of the CRISPR-Cas9 targeted scission on previously dephosphorylated DNA ('targeted mitosequencing'). The sequencing results varied between tissue, species, and integrity of the DNA. The mitoenrichment method yielded 0.17-12.33 % of sequences on target and a mean coverage ranging from 74.9 to 805-fold. The targeted mitosequencing experiment from native genomic DNA yielded 1.83-55 % of sequences on target and a 38 to 2123-fold mean coverage. This produced complete the mitogenome of species with homopolymeric regions, tandem repeats, and gene rearrangements. We demonstrate that deep sequencing of long fragments of native fish DNA is possible and can be achieved with low computational resources in a cost-effective manner, opening the discovery of mitogenomes of non-model or understudied fish taxa to a broad range of laboratories worldwide.</p>
Fig. 2 in Landscape Ecological Analysis Of Taurkalne Forest Tract Fragmentation
Fig. 2. Road network length (km) by category.
FIGURE 1 in Trap-nesting bees and wasps (Hymenoptera, Aculeata) in a Semidecidual Seasonal Forest fragment, southern Brazil
FIGURE 1: Trap nests installed on Parque Estadual São Camilo.
Dumps and Data for 'Compaction during fragmentation and bouncing produces realistic dust grain porosities in protoplanetary discs'
<div>This dataset contains the files required to recreate the Phantom simulations run in the publication Michoulier et al. 2024, A&A 688, A31.</div> <div>We have also included the dumps required to re-create Figures 10, 11, 14, 15, 16, D.1 and E.1.</div>
Surface Enhanced Raman Spectroscopy and Machine Learning for Identification of Beta-Lactam Antibiotics Resistance Gene Fragment in Bacterial Plasmid
<p>Background: The appearance of antibiotic-resistant bacteria represents a critical medical problem with high risk to patient health. Therefore, simple, express, and reliable methods of antibiotic resistance detection should be developed.</p> <p>Results: In this work, we propose a combination of highly sensitive surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) for the detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and the created fragments were captured by functional SERS substrates without preliminary (bio)samples separation or purification. Based on subsequent SERS measurements, a database was created for the training and validation of ML.</p> <p>Significance: The reliability of the proposed method was tested on control samples and we showed the possibility of express SEPS-ML detection of bacterial plasmids containing a characteristic gene up to the 10-7 concentration of the initial plasmid, despite the complex composition of the biological sample (i.e. the presence of the excess of alternative plasmids or various biomolecules). The proposed approach provides a good alternative to modern methods for monitoring antibiotic-resistant bacteria and is favored by its simplicity, low detection limit, and the possibility of express and unpretentious analysis.</p>
Fig. 1 in Avian Assemblages in Forest Fragments do not Sum to the Expected Regional Community in the Brazilian Atlantic Forest.
Fig. 1. Map of Bahia within Brazil, and the location of the fragments under study.
Figure S4 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome
Figure S4. Rarefaction curve for both studied fragments in the Atlantic Forest biome, Brazil. Sample coverage is the proportion of the total number of individuals that belong to the species detected in the sample. F1 = Fragment 1 (28°08′38″S, 54°45′36″W); F2 = Fragment 2 (28°07′33″S, 54°44′57″W).
Figure 3 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome
Figure 3. Variables coefficients and their confidence intervals in the models selected (with ΔAIC ≤ 2) for each small mammal species. (A) Akodon montensis; (B) Oligoryzomys nigripes; (C) Sooretamys angouya; (D) Didelphis albiventris. PC1GC = first axis of the PCA for soil variables; PC2GC = second axis of the PCA for soil variables; PC1VS = first axis of the PCA for vegetation structure; PC2VS = second axis of the PCA for vegetation structure.
IN02055 Thimi Fragment of Inscription (translation)
<p>IN02055 Thimi Fragment of Inscription (translation)</p>
Amarāvatī, Andhra Pradesh. Sculpture fragment.
<p>Amarāvatī, Andhra Pradesh. Sculpture fragment. Chennai, Government Museum.</p>
Amarāvatī, Andhra Pradesh. Railing fragment.
<p>Amarāvatī, Andhra Pradesh, India. Railing fragment. Government Museum, Chennai.</p>
IN02081 Sanku Fragment Inscription (translation)
<p>IN02081 Sanku Fragment Inscription (translation)</p>
Deadwood carbon stocks under logging and fragmentation impacts
<b>Description: </b><p>Wood density estimates of deadwood across decomposition classes</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/23"><b>Deadwood carbon stocks under logging and fragmentation impacts</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=79">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Wood density estimates</b> (Worksheet WoodDensity)</p><p>Dimensions: 418 rows by 23 columns</p><p>Description: Estimates of wood density for deadwood samples</p><p>Fields: </p><ul><li><b>Site</b>: Specific locations where deadwood samples were collected (Field type: Location)</li><li><b>Location</b>: SAFE Project sample block where deadwood samples were collected from (Field type: Location)</li><li><b>Block</b>: SAFE Project sample block where deadwood samples were collected from (Field type: ID)</li><li><b>Samples</b>: Unique identifier for each deadwood item sampled from in the field (Field type: Replicate)</li><li><b>DecayClass</b>: Decomposition level of the deadwood sample (Field type: Ordered Categorical)</li><li><b>SubS</b>: Replicate number. Multiple samples were analysed from a single 'Samples' (Field type: Replicate)</li><li><b>DeadPiece</b>: Type of the deadwood item in the field that the sample for analysis was extracted from (Field type: Categorical)</li><li><b>SpeciesID</b>: Identity of the deadwood item in the field that the sample for analysis was extracted from (Field type: Taxa)</li><li><b>D_upper_cm</b>: Diameter at one end of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>D_lower_cm</b>: Diameter at other end of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>Length_cm</b>: Length of the deadwood item in the field that the sample for analysis was extracted from (Field type: Numeric)</li><li><b>Type</b>: Shape of the deadwood sample being analysed (Field type: Categorical)</li><li><b>MassFresh _ g</b>: Wet weight of the deadwood sample (Field type: Numeric)</li><li><b>L1_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>L2_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>L3_mm</b>: Dimension measurement (Field type: Numeric)</li><li><b>V_fresh_ml</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>MassDry_g</b>: Dry weight of the deadwood sample (Field type: Numeric)</li><li><b>V_dry_ml</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>V_Piece_cm3</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>V_Piece_cm3_set5</b>: Volume of the deadwood sample (Field type: Numeric)</li><li><b>Density</b>: Wood density (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2012-01-01 to 2013-12-31</p><p><b>Latitudinal extent: </b>4.6880 to 4.7436</p><p><b>Longitudinal extent: </b>117.5346 to 117.6290</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Plantae<br> - [Kayu malam]<br> - [Laran]<br> - [Sadaman egari]<br> - [Sedaman]<br> - Tracheophyta<br> -  - Magnoliopsida<br> -  -  - Malpighiales<br> -  -  -  - Euphorbiaceae<br> -  -  -  -  - <i>Macaranga</i><br></div><p></p>
Construct design for the crystallisation of huntingtin fragments - 2018/06/11
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Objective </strong>- Use the cryo-EM structure of huntingtin in complex with HAP40 <a href="http://www.rcsb.org/structure/6EZ8">http://www.rcsb.org/structure/6EZ8</a> to guide construct design of discrete domains and fragments which could be amenable to successful expression and purification of monodisperse protein samples and subsequent structure solution by X-ray crystallography. </p> <p> </p>
Large scale expression and purification of huntingtin fragment A667-C3140 from baculoviral expression system production in sf9 insect cells – 2018/08/02
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Rationale: </strong>To obtain monodisperse and conformationally constrained huntingtin protein samples for high resolution structural biology.</p>
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Allen Brain Atlas
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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.