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1,542 results for “Degradation”
Multi-objective control in human walking: insight gained through simultaneous degradation of energetic and motor regulation systems
<p>See ReadMe.txt</p>
Development of a Standard Test Method for Characterization of Asphalt Modifiers and Aging-Related Degradation Using an Extensional Rheometer
<p>Corresponding data set for Tran-SET Project No. 17BLSU01. Abstract of the final report is stated below for reference:</p> <p>"An extensional deformation test method using a Sentmanat Extensional Rheometer (SER) fixture inside a Dynamic Shear Rheometer (DSR) is developed in this study to investigate the degradation of the polymer due to aging and to investigate the effect of modifier type. A relationship between different percentages of modifier and ductility of the modified binder is also investigated. The sample geometrics used in this study are 1 mm 0.72 mm and 3 mm 0.72 mm. A total of one hundred and sixty-two samples were tested. Three modifiers Styrene-Butadiene-Styrene (SBS), Polyphosphoric Acid (PPA) and latex were used. One PG 76-22, one PG 64-22 and one polymer-modified asphalt emulsion (PAME) were used. First peak elongation force, (F1) is the binders’ stiffness and Second peak elongation force, (F2) is the polymer characteristics. F2 is more visible comparatively at the higher temperature. In most cases, F2 reduces after Rolling Thin Film Oven (RTFO) and Pressure Aging Vessel (PAV) aging. To normalize the stiffness effect of F1 on F2, in this study F2/F1 was used to analyze aging susceptibility of modifiers. All the testing temperatures used in this study exhibited a reduction in F2/F1 due to RTFO aging and further reduction due to PAV aging. Therefore, through this study, it is recommended that this parameter can be used to determine aging susceptibility of polymer in a polymer-modified asphalt binder. F2 is only obtained from the SBS and latex modified binders and emulsions. Addition of PPA did not show any F2, making SBS the most effective modifier among SBS, PPA and latex. F2 has a linear correlation with the percent of the polymer in the PMAE, SBS modified PG 64-22, SBS and PPA modified PG 64-22 and latex modified PG 64-22 with R2 values equal to 0.9934, 0.9323, 0.9893 and 0.9535 respectively, indicating extensional deformation test with SER very promising. Ductility analyses using final angular strain values indicate that modifiers increase ductility significantly while aging reduces ductility. Additional research is required for testing ultra-violet (UV) aged sample, and a DSR-based SER test specification will be developed subsequently."</p>
Occurrence of blood feeding terrestrial leeches in a degraded forest ecosystem
<b>Description: </b><p>This dataset includes the abundance of two species of terrestrial leech collected at multiple sites at the SAFE project in Sabah, Malaysia. Leech collections took place over two seasons, one in the dry season of 2015 and one in the wet season of 2016. For each of the sites, four repeated visits took place and 20 minute searches were conducted within the boundaries of 25 m2 vegetation plots. As these sites have been subjected to differennt degrees of current and historic degradation, the vegetation structure data is also included for each site. For a subset of the leech sites there is corresponding mammal detection data from camera traps across the landscape, which is also included in this dataset.</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/10"><b>The effects of rainforest fragmentation on mammal community assemblages using leech blood-meal analysis</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant , NE/K016148/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3476542">here</a></p><p><b>Files: </b>This consists of 1 file: Drinkwater2019_leech_occurrence.v2.xlsx</p><p><b>Drinkwater2019_leech_occurrence.v2.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Leech abundance and survey-covariates 2015</b> (described in worksheet abundance2015)</p><p>Description: This dataset has the abundance of all the leech individuals of both species collected during surveys in 2015 between February and June. The number of leech collected is split by species of leech and each of the four visits per site. For each survey at a site the associated survey-specific covariates are included. These are the associated effort (number of people collecting the leeches) and the date the visits happened (julian day since the beginning of the year). </p><p>Number of fields: 17</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point (Field type: location)</li><li><b>visit_B1</b>: Number of brown leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_B2</b>: Number of brown leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_B3</b>: Number of brown leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_B4</b>: Number of brown leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>visit_T1</b>: Number of tiger leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_T2</b>: Number of tiger leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_T3</b>: Number of tiger leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_T4</b>: Number of tiger leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>eff_1</b>: Number of people collecting leeches per survey as a measure of survey effort for the first visit to each site (Field type: abundance)</li><li><b>eff_2</b>: Number of people collecting leeches per survey as a measure of survey effort for the second visit to each site (Field type: abundance)</li><li><b>eff_3</b>: Number of people collecting leeches per survey as a measure of survey effort for the third visit to each site (Field type: abundance)</li><li><b>eff_4</b>: Number of people collecting leeches per survey as a measure of survey effort for the fourth visit to each site (Field type: abundance)</li><li><b>date.1</b>: Julian date of visit 1 (Field type: numeric)</li><li><b>date.2</b>: Julian date of visit 2 (Field type: numeric)</li><li><b>date.3</b>: Julian date of visit 3 (Field type: numeric)</li><li><b>date.4</b>: Julian date of visit 4 (Field type: numeric)</li></ul></li><li><p><b>Leech abundance and survey-covariates 2016</b> (described in worksheet abundance2016)</p><p>Description: This dataset has the abundance of all the leech individuals of both species collected during surveys in 2016 between September and December. The number of leech collected is split by species of leech and each of the four visits per site. For each survey at a site the associated survey-specific covariates are included. These are the associated effort (number of people collecting the leeches) and the date the visits happened (julian day since the beginning of the year). </p><p>Number of fields: 17</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point (Field type: location)</li><li><b>visit_B1</b>: Number of brown leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_B2</b>: Number of brown leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_B3</b>: Number of brown leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_B4</b>: Number of brown leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>visit_T1</b>: Number of tiger leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_T2</b>: Number of tiger leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_T3</b>: Number of tiger leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_T4</b>: Number of tiger leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>eff_1</b>: Number of people collecting leeches per survey as a measure of survey effort for the first visit to each site (Field type: abundance)</li><li><b>eff_2</b>: Number of people collecting leeches per survey as a measure of survey effort for the second visit to each site (Field type: abundance)</li><li><b>eff_3</b>: Number of people collecting leeches per survey as a measure of survey effort for the third visit to each site (Field type: abundance)</li><li><b>eff_4</b>: Number of people collecting leeches per survey as a measure of survey effort for the fourth visit to each site (Field type: abundance)</li><li><b>date.1</b>: Julian date of visit 1 (Field type: numeric)</li><li><b>date.2</b>: Julian date of visit 2 (Field type: numeric)</li><li><b>date.3</b>: Julian date of visit 3 (Field type: numeric)</li><li><b>date.4</b>: Julian date of visit 4 (Field type: numeric)</li></ul></li><li><p><b>Site specific covariates</b> (described in worksheet covariates)</p><p>Description: Vegetation structure data associated with each site for which leech surveys were conducted. The metrics include canopy height, moran's I and plant-area-index. These data were extracted from LiDAR data with a 50 m2 buffer around the centroid for each site.</p><p>Number of fields: 6</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point code (Field type: location)</li><li><b>tch</b>: Top of canopy height per site (Field type: numeric)</li><li><b>canopy_height_moran</b>: Habitat heterogeneity - Morans I - per site (Field type: numeric)</li><li><b>canopy_height_sd</b>: Standard deviation of canopy height (Field type: numeric)</li><li><b>pai_mean</b>: Mean plant area index at site (Field type: numeric)</li><li><b>pai_sd</b>: Plant area index standard deviation (Field type: numeric)</li></ul></li><li><p><b>Mammal detections </b> (described in worksheet mammals)</p><p>Description: This dataset contains the mammal detections recorded from camera traps at a subset of the leech survey locations. Sampling effort is also included as a measure of survey effort. </p><p>Number of fields: 27</p><p>Number of data rows: 83</p><p>Fields: </p><ul><li><b>Camera</b>: Name of camera (Field type: location)</li><li><b>CTNs</b>: Measure of trapping effort - number of nights the cameras were operational (Field type: numeric)</li><li><b>Asian Elephant</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Banded Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Banteng</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Bearded Pig</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Bornean Yellow Muntjac</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Common Palm Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Greater Mouse-deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Leopard Cat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Lesser Mouse-deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Long-tailed Macaque</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Long-tailed Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Malay Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Malay Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Marbled Cat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Masked Palm Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Moonrat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Mousedeer sp.</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Muntjac sp.</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Orangutan</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Pig-tailed Macaque</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Red Muntjac</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sambar Deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sun Bear</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sunda Pangolin</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Thick-spined Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2015-02-01 to 2016-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</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> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  -  -  <i>Homo</i> <br> -  -  -  -  -  -  -  <i>Homo sapiens</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Bovidae <br> -  -  -  -  -  -  <i>Bos</i> <br> -  -  -  -  -  -  -  <i>Bos javanicus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  Annelida <br> -  -  -  Clitellata <br> -  -  -  -  Arhynchobdellida <br> -  -  -  -  -  Haemadipsidae <br> -  -  -  -  -  -  <i>Haemadipsa</i> <br> -  -  -  -  -  -  <i>Haemadipsa</i> <br> -  -  -  -  -  -  -  <i>Haemadipsa picta</i> <br></div><p></p>
Figure 4 in Assessing a ReviTec Measure to Combat Soil Degradation by studying Acari and Collembola from Ngaoundéré, Adamawa, Cameroon
Figure 4. Temporal variation of Oribatida and Gamasina in control plots. Details as in Fig. 3.
Data accompanying "In silico analysis of the profilaggrin sequence indicates alterations in the stability, degradation route, and intracellular protein fate in filaggrin null mutation carriers" article.
<p>This research was supported by the National Science Centre, Poland, grant PRELUDIUM number 2021/41/N/NZ1/03473 to NS, National Science Centre, Poland, grant SONATA BIS number 2019/34/E/NZ6/00354 to DG-O, as well as POIR.04.04.00-00-21FA/16–00 grant, carried out within the First TEAM programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund (awarded to DG-O). WP was supported by the National Science Centre, Poland, grant SONATA-BIS number 2021/42/E/NZ1/00190. SB is supported by a Wellcome Trust Senior Research Fellowship (220875/Z/20/Z).</p>
The PIWI-interacting protein Gtsf1 controls the selective degradation of small RNAs in Paramecium
<p><span>Ciliates undergo developmentally programmed genome elimination, in which small RNAs direct the removal of transposable elements during the development of the somatic nucleus. 25-nt scnRNAs are produced from the entire germline genome and transported to the maternal somatic nucleus, where selection of scnRNAs corresponding to germline-specific sequences is thought to take place. Selected scnRNAs then guide the elimination of transposable elements in the developing somatic nucleus. How germline-specific scnRNAs are selected remains to be determined. Here, we provide important mechanistic insights into the scnRNA selection pathway by identifying a <em>Paramecium</em> homolog of Gtsf1 as essential for the selective degradation of scnRNAs corresponding to retained somatic sequences. Consistently, we also show that Gtsf1 is localized in the maternal </span><span>somatic </span><span>nucleus where it associates with the scnRNA-binding protein Ptiwi09. Furthermore, we demonstrate that the scnRNA selection process is critical for genome elimination. We propose that Gtsf1 is required for the coordinated degradation of Ptiwi09-scnRNA complexes that pair with target RNA via the ubiquitin pathway, similarly to the mechanism suggested for microRNA target-directed degradation in metazoans.</span></p>
The conflict between hosts and non-hosts changes the severity of diseases in degraded grassland plant communities
<p>load: disease severity of plant community</p> <p>richnes: species richness</p> <p>Nonhost: Non-hosts richness</p> <p>coverage: relative coverage</p> <p>RHost: relative hosts richness</p> <p>PD: Faith's phylogenetic distance</p> <p>beta: beta diversity of plant community</p> <p>SLA: CWM SLA</p> <p>LN: CWM leaf N content</p> <p>LP:CWM leaf P content</p> <p>NP: CWM leaf N:P</p> <p>group: degree of grassland degradation</p> <p>Shannon: Shannon index of plant community</p> <p>Simpson: Simpsion index of plant community</p> <p>Pielou: Pielou index of plant community</p> <p>marglef: Marglef index of plant community</p>
Dissolved organic matter degradation in the freshwater portion of the St. Lawrence River (2019)
<p>During the summer of 2019, we sampled a 207 km transect of the St. Lawrence, a large temperate river in which flows two strikingly distinct water masses in terms of origin as well as chemical and physical properties. We then assessed dissolved organic matter bio- and photo-reactivity at 40 sites along the river through a series of standardized incubations and exposure to simulated sunlight, and then used water irradiance and morphometric profiles to estimate in situ areal rates of processing across the river. The main variables presented are DOC concentrations and DOM composition data generated by a PARAFAC model. In addition, this dataset also includes results for a suite of standard physical and chemical variables as well as light attenuation profiles obtained with a profiling radiometer.</p>
High Oxygen Exchange Activity of Pristine La0.6Sr0.4FeO3–δ Films and Its Degradation - Dataset
<p>LSF thin film electrodes on YSZ substrates were characterized by electrochemical impedance spectrocopy inside the pulsed laser deposition chamber and in ex-situ emasurement setups. Inside the deposition chamber (in-situ) these electrodes revealed drastically lower polarization resistances. Exposure to several potential degradation sources inside the chamber showed this to be a rather robust effect. A detaield description is available in: https://doi.org/10.1149/1945-7111/abac2b</p> <p>This dataset includes the measured impedance spectra. The directory structure represents the order in which the se spectra appear in the publication. Several spectra are discussed in the context of several effects (e.g pO2 dependency and time dependency). In such cases, they are (redundantly) included in both relevant subdirectories.</p>
DDS (Device-Degraded Speech) Dataset - DAPS portion
<p>DDS (Device-Degraded Speech) dataset provides aligned parallel recordings of high-quality speech (recorded in professional studios) and a large number of versions of low-quality speech, producing approximately 2,000 hours speech data. </p> <p>DDS is built on top of two datasets: DAPS and VCTK. We play clean speech recordings (4 hours from DAPS and 8 hours from VCTK) and re-record waveforms in nine environments (two offices, two conference rooms, three studios, one living room, one waiting room) on three different devices (one MEMS and two condenser microphones), producing 27 different recording conditions. Moreover, each version of condition consists of multiple recordings recorded at 6 different microphone positions to simulate various signal-to-noise ratio (SNR) and reverberation levels. </p> <p><strong>Arxiv: </strong>https://arxiv.org/abs/2109.07931</p> <p> </p> <p><strong>The whole dataset is split into 3 repositories (one part for DAPS portion, two parts for VCTK portion). This repository contains DAPS portion of DDS.</strong></p> <p><strong>For all repository links of DDS v0.8:</strong></p> <ul> <li><strong>DAPS portion:</strong> https://zenodo.org/record/5464104</li> <li><strong>VCTK portion part1:</strong> https://zenodo.org/record/5499506</li> <li><strong>VCTK portion part2:</strong> https://zenodo.org/record/5501697</li> </ul>
Understanding degraded speech leads to perceptual gating of a brainstem reflex in human listeners
<p>The ability to navigate "cocktail-party" situations by focussing on sounds of interest over irrelevant, background sounds is often considered in terms of cortical mechanisms. However, subcortical circuits such as the pathway underlying the medial olivocochlear (MOC) reflex modulate the activity of the inner ear itself, supporting the extraction of salient features from auditory scene prior to any cortical processing. To understand the contribution of auditory subcortical nuclei and the cochlea in complex listening tasks, we made physiological recordings along the auditory pathway while listeners engaged in detecting non(sense)-words in lists of words. Both naturally spoken and intrinsically noisy, vocoded speech—filtering that mimics processing by a cochlear implant—significantly activated the MOC reflex, but this was not the case for speech in background noise, which more engaged midbrain and cortical resources. A model of the initial stages of auditory processing reproduced specific effects of each form of speech degradation, providing a rationale for goal-directed gating of the MOC reflex based on enhancing the representation of the energy envelope of the acoustic waveform. Our data reveals the co-existence of two strategies in the auditory system that may facilitate speech understanding in situations where the signal is either intrinsically degraded or masked by extrinsic acoustic energy. Whereas intrinsically degraded streams recruit the MOC reflex to improve representation of speech cues peripherally, extrinsically masked streams rely more on higher auditory centres to de-noise signals.</p>
Insect RTUs from the degraded forest fragments in the Attappady and Anaikatti landscapes.
<p>Datasets were collected as part of the project titled "EVALUATING THE EFFICEINCY OF RESTORATION EFFORTS IN REVIVING TROPICAL FORESTS USING GROUND INSECTS AS INDICATORS."</p>
Processed Data for Figures of "Declining Amazon biomass due to deforestation and subsequent degradation losses exceeding gains"
<p>Processed output data for generating figures of "Declining Amazon biomass due to deforestation and subsequent degradation losses exceeding gains". Most datasets are aggregated to 0.25 degree resolution of L-VOD data.</p> <p>Data are organised in folders but should be added to a single directory for easy use with provided code.</p> <ul> <li>AGC_LVOD: Aboveground carbon [Mg C ha] (2011-2019) and trends [Mg C ha yr] derived from different L-VOD indices calibrated to AGC using the ESA CCI biomass map. Provides data based on smoothed, max and trend indices as well as their mean.</li> <li>modelprocesschanges: Annual and total aboveground carbon changes [Mg C ha] (2011-2018) derived for different processes following methods described in the manuscript.</li> <li>inputData: pre-processed landcover fraction information at 1 km resolution, used as input for Figure 1b.</li> <li>forestcover: Forest cover and intact (old-growth) forest cover fractions derived from mapbiomas. (2011-2018)</li> <li>masks: Raster mask of areas considered in analysis.</li> <li>valuerangeserrors: Upper and lower value boundaries for AGC and processes, derived from standard errors in ESA CCI biomass map.</li> </ul> <p>When using any of the data listed above please cite the original paper (Fawcett et al., 2022) as well as the original data sources mentioned in the manuscript.</p>
Madagascar's fire regimes challenge global assumptions about landscape degradation
<p><span><span>Fire and environmental dataset (2003 - 2019) for Phelps et al. (2022, Global Change Biology). <br>Associated manuscript abstract: Narratives of landscape degradation are often linked to unsustainable fire use by local communities.</span><span> Madagascar is a case in point: the island is considered globally exceptional, with its remarkable endemic biodiversity seen as threatened by unsustainable anthropogenic </span><span>fire. Yet, fire regimes on Madagascar have not been empirically characterised or globally contextualised. Here, we apply a comparative approach using MODIS remote sensing data (2003-2019), to determine relationships between Madagascar's fire regimes and global patterns and trends. We demonstrate that Madagascar's fire regimes are similar to 88% of tropical burned area, with shared climate and vegetation characteristics. Therefore, rather than a global exception, Madagascar's fire regimes could usefully be understood as a microcosm of most tropical fire regimes, which contribute to global understanding of fire. We found that landscape-scale fire declined in grassy biomes across the tropics, and at a relatively fast rate on Madagascar. The island's high tree loss anomalies (1.25 to 4.77x the tropical average) were not explained by any general expansion of grassy biome burning and were centred in forests rather than at forest-savanna boundaries, demonstrating that high rates of forest degradation were not explained by landscape-scale fire escaping from savannas into forests. Associated with forests, landscape-scale fire trends reflected important differences among tropical regions, indicating a need to better understand regional variation in the anthropogenic drivers of change. Unexpectedly, the highest tree loss anomalies on Madagascar were centred in environments </span><span>without </span><span>landscape-scale fire, where the role of small-scale fires (<21ha) is unknown. Madagascar's fire regimes thus contribute two lessons with global implications: first, landscape-scale burning is declining in grassy biomes across the tropics and does not explain high tree loss anomalies on Madagascar. Second, landscape-scale fire is not uniformly associated with forest loss, indicating a need for more socio-ecological context around narratives of tropical fire and ecosystem degradation. </span></span></p>
Climate warming causes photobiont degradation and C starvation in a boreal climate sentinel lichen
<p>The long-term potential for acclimation by lichens to warming climates is poorly known, despite their prominent roles in forested ecosystems. Although often considered "extremophiles", lichens may not readily acclimate to novel climates well beyond historical norms. In a previous study (Smith et al. 2018), Evernia mesomorpha transplants in a whole-ecosystem climate change experiment showed drastic mass loss after one year of warming. We examined the causes of this warming-induced mass loss by measuring physiological, functional, and reproductive attributes of lichen transplants. Severe loss of mass and physiological function occurred above +2ºC of warming. Loss of algal symbionts ("bleaching") and turnover in algal community compositions increased with temperature and were the clearest impacts of experimental warming. Enhanced CO 2 had no significant physiological or symbiont composition effects. The functional loss of algal photobionts led to significant loss of mass and specific thallus mass (STM), which in turn reduced water-holding capacity (WHC). Although algal genotypes remained detectable in thalli exposed to higher temperatures, within-thallus photobiont communities shifted in composition towards greater diversity. Analogous to the effects of climate change on corals, the balance of symbiont carbon metabolism in lichens is central to their resilience to changing conditions.</p>
Changes in plant community assembly from patchy degradation of grasslands and grazing by different-sized herbivores
<p>Grassland degradation caused by increases in livestock grazing threatens a variety of ecosystem services. Understanding changes in plant community assembly during the process of grassland degradation in the presence of grazing is important to help restore degraded grasslands worldwide but has received little attention thus far. The grassland degradation process is typified by heterogeneous degradation, i.e., gradual formation of degraded patches (hereafter "patchy degradation"). Here, we experimentally examined the effects of herbivore grazing and patchy degradation on plant community assembly using nine pairs of non-degraded (intact) and patch-degraded (fragmented) grasslands subject to grazing by different-sized herbivores (i.e., NG, no grazing; SG, sheep grazing; CG, cattle grazing) over four years. Using a null-model approach, we estimated the relative magnitude of deterministic processes of community assembly by comparing the observed and expected β-diversity. We found that in the absence of herbivore grazing, deterministic processes played a greater role in community assembly, regardless of whether patchy degradation had occurred. However, the deterministic processes resulted in plant communities being more spatially similar in non-degraded grasslands while being more dissimilar in patchy degraded grasslands. Compared with non-degraded grasslands, species with strong competitive abilities (i.e., Leymus chinensis) were less dominant in patchy degraded grasslands, indicating relaxed competition and a reduced role of species interactions over plant communities. Instead, patchy degradation added the role of environmental variables over plant communities. Sheep grazing consistently promoted more stochastic plant community assembly in both non-degraded and patch-degraded grasslands, while cattle grazing promoted more stochastic plant community assembly only in the non-degraded state, having no effect in the patch-degraded state. Our study offers important insights into changes in plant community assembly during ongoing patch-degradation of grasslands, indicating the role of increased environmental filtering of soil and reduced species interactions in driving plant community dynamics with increasing grassland patchy degradation. We also uncovered an herbivore species-specific effect on plant community assembly during the process of grassland degradation, which will better inform and improve future grassland restoration planning efforts.</p>
Dataset for Symptoms of performance degradation during multi-annual drought: a large-sample, multi-model study
<p>This dataset contains the data described in the journal article "Symptoms of performance degradation during multi-annual drought: a large-sample, multi-model study" (Trotter et al., 2023, <strong>DOI: </strong>10.1029/2021WR031845)</p>
Rare soil microbial taxa regulate the negative effects of land degradation drivers on soil organic matter decomposition
<p>1. Land degradation drivers, including loss in vegetation and eutrophication, are expected to impact soil biodiversity and functions in drylands world-wide. Soils contain both common and rare microbial taxa that drive multiple soil functions. Yet, little is known about how these microbial taxa influence the impacts of land degradation drivers on ecosystem functions. Obtaining this information is essential to determine whether rare taxa need to be protected, or if protecting only common taxa would be enough to sustain and protect ecosystem functions and services.</p> <p>2. Here, we conducted an experiment to investigate the effects of N-enrichment and vegetation loss (plant removal), which are two major land degradation drivers in semi-arid grasslands, on the diversities of common and rare soil bacterial and fungal taxa and soil function [soil organic matter (SOM) decomposition] in a long-term experiment.</p> <p>3. Six years after N-enrichment and vegetation loss, we found that N-enrichment decreased the alpha diversities of common and rare soil bacteria and rare soil fungi, while vegetation loss only decreased the alpha diversity of rare soil fungi. Both N-enrichment and vegetation loss altered the community composition of common and rare bacteria and fungi, except for the lack of response of common soil fungi to the vegetation loss. Moreover, both structural equation modelling and variation partitioning analyses show that land degradation drivers reduce SOM decomposition, and these were also indirectly associated with changes in the diversity of rare microbial taxa, especially that of bacteria.</p> <p>4.<em> Synthesis and applications</em>. Collectively, this work shows that land degradation can have negative impacts on soil biodiversity and functions, and the rare microbial taxa indirectly regulate the impacts of land degradation on ecosystem functioning. These results indicate that the rare microbial taxa can be used as one of the ecological indicators for identifying land degradation in the semi-arid grasslands. These findings are essential to understand the future impacts of desertification and land degradation on rare microbial taxa–function relationships in global drylands.</p>
Data for "Structure and mechanism of oxalate transporter OxlT in an oxalate-degrading bacterium in the gut microbiota"
<p>MD simulation data of OxlT. Trajectory data and NAMD input files are included. The first 500 ns trajectory that starts from the occluded conformation with a transition to the outward-open conformation is in the OxlT-occ directory. The 200 ns trajectories that start from the outward-open conformation with different protonation states of K355 are in the OxlT-out directory. </p>
Data from: Plant–moth community relationships at the degraded urban peat-bog in Central Europe
<p>Peatlands have their own, specific insect fauna. They are a habitat not only for ubiquistic but also stenotopic moths that feed on plants limited to wet, acid and oligotrophic habitats. In the past, raised bogs and fens were widely distributed in Europe. This has changed since 20th c. Due to irrigation, modern forestry and increasing human settlement, peatlands have become isolated islands in agricultural and urban landscape. Here we analyse the flora in a degraded bog situated in a large Lodz city agglomeration in Poland in relation to the diversity and composition of moth fauna. Over the last 40 years since the bog has been protected as a nature reserve, birch, willow and alder shrubs replaced the typical raised bog plant communities due to the decreased water level. The analysis of moth communities sampled in 2012 and 2013 indicates dominance of ubiquistic taxa associated with deciduous wetland forests and rushes. Tyrphobiotic and tyrphophile moth taxa were not recorded. We conclude that the absence of moths typical of bog habitats and the dominance of common, woodland species are associated with hydrological changes, the expansion of trees and brushes over typical bog plant communities and light pollution.</p>
ScienceDex guides
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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.