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4,010 results for “STABILITY”
Figure 1 in Stability and acceleration of phenotypic evolution in spiny rats (Trinomys, Echimyidae) across different environments
Figure 1. Skull of Trinomys. Dorsal (top), ventral (center) and lateral (bottom) views illustrating the 18 linear measurements used in the present study. GSL, greatest skull length; LN, length of nasals; LR, length of rostrum; IOC, least interorbital constriction; WZ, width of zygomatic arches; WM, width of mastoids; BAL, basilar length; LIF, length of incisive foramen; D, length of diastema; PLA, palatal length A; PLB, palatal length B; LMS, length of molar series; PPL, post-palatal length; LBu, length of auditory bulla; HB, height of braincase; WMax, width of maxillary; WR, width of rostrum; DR, depth of rostrum. Figure modified from Nicola et al. (2003).
Figure 4 in Stability and acceleration of phenotypic evolution in spiny rats (Trinomys, Echimyidae) across different environments
Figure 4. Mean estimates of pairwise rates of phenotypic change (J) along the size axis (A) and multivariate axes PC1 (B), PC2 (C) and PC3 (D), and mean estimates of pairwise rates of general phenotypic change (J0) based in Mahalanobis distances (E). Columns represent mean estimates and error bars 95% confidence intervals. AF X AF, estimates between Atlantic Forest species; AF X y, estimates between Atlantic Forest species and T. yonenagae; AF X a, estimates between Atlantic Forest species and T. albispinus.
Figure 3 in Stability and acceleration of phenotypic evolution in spiny rats (Trinomys, Echimyidae) across different environments
Figure 3. Scores of individuals of Trinomys plotted in morphospaces formed by the size axis vs. PC1 (A), PC2 vs. PC3 (B) and CV1 vs. CV2 (C). Trinomys eliasi, T. albispinus and T. yonenagae are enclosed in convex grey polygons.
Is the age of plant communities predicted by the age, stability and soil composition of the underlying landscapes? An investigation of OCBILs
<p></p><p>Old, climatically buffered, infertile landscapes (OCBILs) have been hypothesized to harbour an elevated number of persistent plant lineages and are predicted to occur across different parts of the globe, interspersed with other types of landscapes. We tested whether the mean age of a plant community is associated with occurrence on OCBILs, as predicted by climatic stability and poor soil environments. Using digitized occurrence data for seed plants occurring in Australia (7033 species), sub-Saharan Africa (3990 species) and South America (44 482 species), regions that comprise commonly investigated OCBILs (Southwestern Australian Floristic Region, Greater Cape Floristic Region and campos rupestres), and phylogenies pruned to match the species occurrences, we tested for associations between environmental data (current climate, soil composition, elevation and climatic stability) and two novel metrics developed here that capture the age of a community (mean tip length and mean node height). Our results indicate that plant community ages are influenced by a combination of multiple environmental predictors that vary globally; we did not find statistically strong associations between the environments of OCBIL areas and community age, in contrast to the prediction for these landscapes. The Cape Floristic Region was the only OCBIL that showed a significant, although not strong, overlap with old communities.</p><p></p>
Boosting Activity and Stability of Metal Single-Atom Catalysts via Regulation of Coordination Number and Local Composition
<p>Optimized geometry coordinates in DFT calculations for the publication "Boosting Activity and Stability of Metal Single-Atom Catalysts via Regulation of Coordination Number and Local Composition".</p> <p> </p>
All data support the published articel "Loop-optimization of Trichoderma reesei endoglucanases for balancing the activity–stability trade-off through cross-strategy between machine learning and the B-factor analysis"
<p><em>Trichoderma reesei</em> endoglucanases (EGs) have limited industrial applications due to its low thermostability and activity. Here, we aimed to improve the thermostability of EGs from<em> T.reesei</em> without reducing its activity counteracting the activity-stability trade-off. A cross-strategy combination of machine learning and B-factor analysis was used to predict beneficial amino acid substitution in EG loop optimization. Experimental validation showed single-site mutated EG concomitantly improved enzymatic activity and thermal properties by 17.21%–18.06% and 49.85%–62.90%, respectively, compared with wild-type EGs. Furthermore, the mechanism explained mutant variants had lower RMSD values and a more stable overall structure than the wild type. According to this study, EGs loop optimization is crucial for balancing the activity-stability trade-off, which may provide new insights into how loop region function interacts with enzymatic characteristics. Moreover, the cross-strategy between machine learning and B-factor analysis improved superior enzyme activity-stability performance, which integrated structure-dependent and sequence-dependent information.</p>
Dataset: "Resilience of small PAHs in interstellar clouds: Efficient stabilization of cyanonaphthalene by fast radiative cooling"
<p>Data supporting the article "Resilience of small PAHs in interstellar clouds: Efficient stabilization of cyanonaphthalene by fast radiative cooling"</p>
Data for: Implications of plant N/P stoichiometry influenced by arbuscular mycorrhizal fungi for stability of plant species and community in response to nutrient limitation
<p>Arbuscular mycorrhizal fungi (AMF) influence plant nitrogen/phosphorus (N/P) by modifying plant N and P uptake, which further affects plant stoichiometric N/P homeostasis. Plant species and community stoichiometric N/P homeostasis can impact plant species and community stability, respectively, in response to variations of soil N and P availabilities. We investigated interspecific plant interactions via AMF in regard to plant and soil microbial N/P stoichiometry across different soil N and P availabilities induced by N and P addition (0 mg N kg<sup>-1</sup>, 25 mg N kg<sup>-1</sup>, 50 mg N kg<sup>-1</sup>, 30 mg P kg<sup>-1</sup>, and 100 mg P kg<sup>-1</sup>). We selected one dominant (<em>Bothriochloa ischaemum</em>; C4 grass) and one subordinate (<em>Lespedeza davurica</em>; legume) species in a natural grassland climax community. We examined how AMF influences stoichiometric N/P homeostasis in monoculture and mixed culture systems, and the resulting consequences for temporal stability of plant species and community in response to variations in soil N and P availability.</p> <p>The AMF mitigated the P limitation of soil microbial communities and decreased the degree of stoichiometric N/P homeostasis of host plants in monoculture. Through their resource-scavenging and soil organic matter mineralisation functions, AMF enhances plant 'luxury consumption', promoting species stability in monoculture in response to soil N and P availability variations. Compared with plants in monoculture, the interaction between <em>B. ischaemum</em> and <em>L. davurica</em> via AMF increased shoot N/P under soil N-poor conditions, leading to an enhanced degree of stoichiometric N/P homeostasis in both plant species, especially the legume.</p> <p>Our results suggest that interspecific plant interaction between C4 grass and legume mediated by AMF confers an advantage in complementarity in plant N acquisition under N-poor conditions, leading to increased stability of plant communities and better maintenance of subordinate species (legume) in response to soil N deficiency.</p>
Mean wind and stability time series at FINO1 between June and July 2015 (from OBLEX-F1)
<p>Data collected from a sonic anemometer mounted at 15m above the mean sea level at the geographical location of FINO1 offshore met-mast. Time series cover yeardays between 160 and 210, 2015.</p> <p>% % Datasets are as follows:</p> <p>% % time15: mean time at each 30-min burst of 15m sonic data.</p> <p>% % umean15: mean wind speed at 15m height from average of each sonic 30-min segment at 15m sonic.</p> <p>% % UU10: wind at 10m heigh from umean15 using logarithmic wind profile.</p> <p>% % ti_ref: reference time - first of January 2015.</p> <p>% % wdir: wind direction at 15m measured from high freq. sonic data.</p> <p>% % wa: wave age data estimated from sonic data and wave phase speed (see OBLEX-F1 data).</p> <p>% % L15: Moninnon-Obukhov length scale measured by 25Hz sonic data at 15m height.</p> <p>% %</p> <p>% % Mostafa Bakhoday Paskyabi, 10 Dec. 2022</p> <p>% % Mostafa.Bakhoday-Paskyabi@uib.no</p> <p> </p> <p> </p>
Phenotypic stability in scalar calcium of freshwater fish across a wide range of aqueous calcium availability in nature
<div> <p>Spatial environmental gradients can promote adaptive differences among conspecific populations as a result of local adaptation or phenotypic plasticity. Such divergence can be opposed by various constraints, including gene flow, limited genetic variation, temporal fluctuations, or developmental constraints. We focus on the constraint that can be imposed when some populations are found in locations characterized by low levels of an essential nutrient. We use scales of wild fish to investigate phenotypic effects of spatial variation in a potentially limiting nutrient – calcium. If scale calcium (we use "scalar" calcium for consistency with the physiology literature) simply reflects environmental calcium availability, we expect higher levels of scalar calcium in fish from calcium-rich water, compared to fish from calcium-poor water. To consider this "passive response" scenario, we analysed scalar calcium concentrations from three native fish species (<em>Lepomis gibbosus</em>, <em>Percina caprodes</em>, and <em>Perca flavescens</em>) collected at multiple sites across a dissolved calcium gradient in the Upper St. Lawrence River. Contradicting the "passive response" scenario, we did not detect strong or consistent relationships between scalar calcium and water calcium. Instead, for a given proportional increase in water calcium across the wide environmental gradient, the corresponding proportional change in scalar calcium was much smaller. We thus favor the alternative "active homeostasis" scenario, wherein fish from calcium-poor water are better able to uptake, mobilize, and deposit calcium than are fish from calcium-rich water. We further highlight the importance of studying functional traits, such as scales, in their natural setting as opposed to only laboratory studies.</p> </div>
ATM-ESCO2-SMC3 axis promotes 53BP1 recruitment in response to DNA damage and safeguards genome integrity by stabilizing cohesin complex
<p>53BP1 is primarily known as a key regulator in DNA double-strand break (DSB) repair. However, the mechanism of DSB-triggered cohesin modification-modulated chromatin structure on the recruitment of 53BP1 remains largely elusive. Here we identified acetyltransferase ESCO2 as a regulator for DSB-induced cohesin-dependent chromatin structure dynamics, which promotes 53BP1 recruitment. Mechanistically, in response to DNA damage, ATM phosphorylates ESCO2 S196 and T233. MDC1 recognizes phosphorylated ESCO2 and recruits ESCO2 to DSB sites. ESCO2-mediated acetylation of SMC3 stabilizes cohesin complex conformation and regulates the chromatin structure at DSB breaks, which is essential for the recruitment of 53BP1 and the formation of 53BP1 microdomains. Furthermore, depletion of ESCO2 in both colorectal cancer cells and xenografted nude mice sensitizes cancer cells to chemotherapeutic drugs. Collectively, our results reveal a molecular mechanism for the ATM-ESCO2-SMC3 axis in DSB repair and genome integrity maintenance with a vital role in chemotherapy response in colorectal cancer.</p>
Seasonal community stability increased with water addition and shrub removal but reduced with nitrogen addition in semi-arid grassland
<p>1. Stability is a useful indicator of the functioning and sustainability of an ecosystem, and many studies have explored the effects of anthropogenic disturbance on the inter-annual stability of plant communities. However, the effects of multiple anthropogenic stressors on seasonal community stability have not been clearly elucidated, especially for vulnerable semi-arid grasslands.</p> <p>2. During the growing season in the 5th year of the experiment, we determined how nitrogen (N) addition, water addition, and shrub removal altered seasonal community stability in a semi-arid grassland dominated by the shrub <em>Caragana</em> <em>microphylla</em> on the Mongolian Plateau.</p> <p>3. We found that shrub removal, N addition, and water addition had different effects on the stability of the community and plant functional groups (PFGs). Shrub removal increased seasonal community stability mostly via increases in the stability of perennial forbs and C4 plants, and shrub removal did not alter the effects of N addition or water addition on seasonal community stability or PFG stability.</p> <p>4. N addition decreased seasonal community stability mostly via decreases in the stability of perennial rhizome grasses and C<sub>4</sub> plants. Water addition increased seasonal community stability mostly via increases in the stability of annuals and biennials, perennial forbs, perennial rhizome grasses, dominant species, and C<sub>4</sub> plants. Species asynchrony and PFG stability but not species richness or soil abiotic or biotic variables helped to maintain seasonal community stability under N addition or water addition.</p> <p>5. Our findings indicate that future scenarios of increases in N deposition and shrub encroachment will strongly reduce community stability in drylands, and that future scenarios of increases in precipitation together with shrub removal might help to maintain the stability of this and other dryland ecosystems.</p>
Preference of CAMSAP3 for expanded microtubule lattice contributes to stabilization of the minus end
<p>Dataset for the publication.</p> <p>biorxiv: https://www.biorxiv.org/content/10.1101/2022.08.21.504720v1</p>
Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication
<p><strong>This repository contains the dataset linked to the following publication:</strong></p> <p><strong>Article title: </strong>Synergistic effects of warming and internal nutrient loading interfere with the long-term stability of lake restoration and induce sudden re-eutrophication</p> <p><strong>Journal: </strong><em>Environmental Science & Technology</em></p> <p><strong>DOI</strong>: 10.1021/acs.est.2c07181</p> <p><strong>Abstract:</strong> Phosphorus (P) precipitation is among the most effective treatments to mitigate lake eutrophication. However, after a period of high effectiveness, studies have shown possible re-eutrophication and the return of harmful algal blooms. While such abrupt ecological changes were attributed to the internal P loading, the role of lake warming and its potential synergistic effects with internal loading, thus far, has been understudied. Here, in a eutrophic lake in central Germany, we quantified the driving mechanisms of the abrupt re-eutrophication and cyanobacterial blooms in 2016 (30 years after the first P precipitation). A process-based lake ecosystem model (GOTM-WET) was established using a high-frequency monitoring dataset covering contrasting trophic states. Model analyses suggested that the internal P release accounted for 68% of the cyanobacterial biomass proliferation, while lake warming contributed to 32%, including direct effects via promoting growth (18%) and synergistic effects via intensifying internal P loading (14%). The model further showed that the synergy was attributed to prolonged lake hypolimnion warming and oxygen depletion. Our study unravels the substantial role of lake warming in promoting cyanobacterial blooms in re-eutrophicated lakes. The warming effects on cyanobacteria via promoting internal loading need more attention in lake management, particularly for urban lakes.</p> <p><strong>SYNOPSIS: </strong>Warming synergistically promotes re-eutrophication with internal nutrient loading and exacerbates cyanobacterial blooms in urban lakes 30 years after phosphorus mitigation.</p> <p> </p> <p><strong>Data description </strong>by Xiangzhen Kong (<a href="mailto:xzkong@niglas.ac.cn">xzkong@niglas.ac.cn</a>), 2023-02-20</p> <p>---Wet chemical analysis on water samples taken at five depths (0.5, 2.5, 5.0, 7.0 and 9.0 m) from the deepest point in the lake (BA1) at biweekly intervals from 2018.5-2021.8.</p> <p> File name:</p> <ul> <li>BAB_BA1_TN_mgL.obs (total nitrogen concentration)</li> <li>BAB_BA1_NH4_mgL.obs (ammonium nitrogen concentration)</li> <li>BAB_BA1_NO3_mgL.obs (nitrate nitrogen concentration)</li> <li>BAB_BA1_TP_mgL.obs (total phosphorus concentration)</li> <li>BAB_BA1_SRP_mgL.obs (Soluble reactive phosphorus concentration)</li> <li>BAB_BA1_DP_mgL.obs (dissolved P concentration)</li> <li>BAB_BA1_DOC_mgL.obs (Dissolved organic carbon concentration)</li> <li>BAB_BA1_Si_mgL.obs (dissolved silicon concentration)</li> <li>BAB_BA1_Chla_HPLC_DIN_mgL.obs (Chl-a concentration)</li> </ul> <p> </p> <p>---CTD probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution</p> <p> File name:</p> <ul> <li>t_prof_file_barleber_ctm644.obs (water temperature)</li> <li>oxy_prof_file_barleber_ctm644 (Dissolved oxygen)</li> <li>turb_prof_file_barleber_ctm644.obs (Turbidity)</li> <li>chla_prof_file_barleber_ctm644.obs (Chl-a concentration)</li> </ul> <p> </p> <p>---BBE probe profile data from the deepest point in the lake (BA1) from 2017.8 to 2021.8 at biweekly basis with approximately 0.1 m vertical resolution</p> <p> File name:</p> <ul> <li>totalChla_prof_file_barleber_FP2101.obs (Chl-a concentration)</li> <li>bluegreen_prof_file_barleber_FP2101.obs (Blue-green algae Chl-a concentration)</li> <li>green_prof_file_barleber_FP2101.obs (Green algae Chl-a concentration)</li> <li>diatom_prof_file_barleber_FP2101.obs (Diatom Chl-a concentration)</li> </ul> <p> </p> <p> </p>
Tree community composition stabilizes ecosystem functions in response to drought
In summer 2018, Central Europe was hit by an extreme drought event that widely impacted ecosystems and markedly increased tree mortality in forest ecosystems across the continent. As climate models predict an increase in frequency and severity of such events, there is an urgent need to adapt forests in order to maintain the diverse benefits they provide. Soil processes play an essential role in this context and are key for a plethora of terrestrial ecosystem functions but are strongly dependent on water availability. Here we investigated how tree species richness, composition, and identity in a 13-year-old temperate tree diversity experiment influenced selected ecosystem functions (as important representatives of different ecosystem processes) during the 2018 summer drought. We focused on the stability of soil microbial biomass and standard litter decomposition, as well as tree species-specific mortality rates. Contrary to our expectations, tree species richness did not generally increase the resistance of soil functions and decrease tree mortality rates. However, the resistance of these functions was determined by tree species identity and community composition. For the resistance of both soil functions (microbial biomass and litter decomposition), we found that tree species richness effects depended on the presence of certain tree species. Moreover, we found that the performance of a specific tree species in monoculture, Norway Spruce, was a poor predictor of its response to drought in tree species mixtures. Taken together, the results of our study demonstrate that the species composition of tree stands determines tree mortality and the resistance of soil functions under drought. This indicates that enhancing multiple ecosystem functions under environmental disturbance requires maintaining diverse forests.
Supporting Data for: "Additivity of atomic strain fields as a tool to strain-engineering phase-stabilized CsPbI$_3$ perovskites"
<p>This dataset contains every training and validation datasets ase well as config YAML files that were used to train the NequIP MLPs in the paper titles as "Additivity of atomic strain fields as a tool to strain-engineering phase-stabilized CsPbI$_3$ perovskites".</p> <p>Also python scripts for MLP-MD and strain field analysis are supplied.</p>
Dataset: Bed Stability of Step-Pool Channels with Macrorough Sidewalls
<p>The data set includes the cumulative sediment outflow data and the digital elevation model (DEM) data of flume experiments conducted at the Laboratory of Hydraulics, Hydrology, and Glaciology (VAW) at ETH Zurich.</p>
Dataset for "Gap junctions desynchronize a neural circuit to stabilize insect flight"
<p><strong>Data guide</strong></p> <p>For each figure, we provide one data folder. Each folder contains one overview Excel file that provides all source data that is shown in the respective figure. For some figure panels additional subfolders with original data files are provided. Please note that the data for figures 3A-J (except F and G) have been uploaded together with the analyses and simulation code and are accessible under the doi: 10.5281/zenodo.7740678</p> <p> </p> <p><strong>Data Figure 1: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 1.xlsx</li> <li>Folder Panel A: <ul> <li>Recordings shown in A and G with sorted spikes: figure 1a_original recording.smr</li> <li>Data matrix of waveforms shown in A as text file: figure 1a_waveforms_200818_561.5-566.5sec.txt</li> </ul> </li> <li>Folder Panels B+F: all original recordings with sorted spikes that have been analyzed for panels B and F: <ul> <li>171106_m1.SMR</li> <li>181029_m1.smr</li> <li>190522_m1.SMR</li> <li>200305_m1.smr</li> <li>200818_m1.smr</li> <li>200824_m1.smr</li> <li>200827_m1.SMR</li> <li>201005_m1.smr</li> </ul> </li> <li>Folder Panel C: all original recordings as .smr (Spike2) files that went into the analyses shown in figure 1c. <ul> <li>100 recordings in folders “Fly 1 to Fly 100”</li> <li>1 recording in folder “single animal”</li> <li>the waveforms of these 101 animals are also as text in file Figure 1.xlsx</li> </ul> </li> <li>Folder Panel D <ul> <li>The original data file of the recording shown in figure 1D as axon file: 20128002_OUT1_firing.abf</li> <li>The selected waveforms shown in figure 1D are also as text in file Figure 1.xlsx</li> </ul> </li> <li>All recordings that underlie the analysis in figure 1E are as number matrices in the file Figure 1.xlsx</li> <li>The spike events shown in figure 1G are as number matrices in the file Figure 1.xlsx</li> </ul> <p> </p> <p><strong>Data Figure 2: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 2.xlsx</li> <li>The number matrices for all traces shown in figure 2A are as number matrices in the file Figure 2.xlsx</li> <li>Panel B: all number matrices of the event times that underlie the analyses shown in figure 2B: <ul> <li>Folder “control”: .txt of event times of the 7 control animals</li> <li>Folder “ShakB-RNAi”: .txt of event times of the 7 ShakB-RNAi-kd animals</li> <li>Folder “ShakB-oe”: .txt of event times of the 7 ShakB-oe (overexpression) animals</li> </ul> </li> <li>Panel C original confocal image stack for image data shown in figure 2C: <ul> <li>original Leica file: figure 2c_image stack.lif</li> <li>Folder “tiff” with same image stack saved as tiff</li> </ul> </li> </ul> <p> </p> <ul> <li>Panel D: The number matrices underlying the waveforms of the traces shown in Figure 2D are contained in the file Figure 2.xlsx</li> <li>Panel E: The number matrices underlying the waveforms of the traces shown in Figure 2E are contained in the file Figure 2.xlsx</li> <li>Panel F: The number matrices of all pre- and postsynaptic voltage areas of paired MN recordings and the analyses thereof for Figure 2F (left) are contained in the file Figure 2.xlsx. The number matrices of all pre- and postsynaptic voltage amplitudes of paired MN recordings and the analyses thereof for Figure 2F (right) are contained in the file Figure 2.xlsx</li> <li>Panel G: The number matrices underlying the waveforms of the traces shown in Figure 2G are contained in the file Figure 2.xlsx</li> <li>The number matrices underlying the waveforms of the traces shown in Figure 2H are contained in the file Figure 2.xlsx</li> <li>The number matrices underlying the waveforms of the traces shown in Figure 2I are contained in the file Figure 2.xlsx</li> </ul> <p> </p> <p><strong>Data Figure 3: </strong>(all data for Fig. 3 except f and g are at doi: 10.5281/zenodo.7740678)</p> <ul> <li>Overview Excel file with all source data for Figures 3G and F: Figure 3.xlsx</li> <li>Panel F: The number matrices for all traces shown in figure 3F (command voltage +50mV) and for 14 additional command voltage steps are contained in the file Figure 3.xlsx</li> <li>Panel F: The number matrices for the waveforms of all traces shown in figure 3F (command voltage +50mV) and for 14 additional command voltage steps are contained in the file Figure 3.xlsx</li> <li>Panel G: The number matrices for the waveforms of all traces shown in figure 3G are contained in the file Figure 3.xlsx <ul> <li>original data file from which the traces in figure 3G (top) were taken: control MN4_3_0s-87s.SMR</li> <li>original data file from which the traces in figure 3G (bottom) were taken: UAS shab81 2022_10_07_0s-87s.SMR</li> </ul> </li> </ul> <p> </p> <p><strong>Data Figure 4: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 4.xlsx</li> <li>Panel A: The number matrices for all event signals and all waveform traces shown in figure 4A are as number matrices in the file Figure 4.xlsx</li> <li>Figure 4B (top): <ul> <li>The number matrices for the waveforms (voltage recording and fit) shown in Figure 4b (top) are contained in the file Figure 4.xlsx</li> <li>The number matrices for the waveforms of the voltage recordings that were fitted to obtain the averages shown in Figure 4B top are contained in the file Figure 4.xlsx</li> <li>The fitting parameters and results for Figure 4B (top) are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4B (middle): <ul> <li>The number matrices for the waveforms (calcium imaging and fit) shown in Figure 4b (middle) are contained in the file Figure 4.xlsx</li> <li>The fitting parameters and results for Figure 4B (middle) are contained in the file Figure 4.xlsx</li> <li>The original imaging data for this analysis is contained in movie S4</li> </ul> </li> <li>Figure 4B (bottom): <ul> <li>The number matrices for the waveforms (wingbeat frequency from events and fit) shown in Figure 4b (bottom) are contained in the file Figure 4.xlsx</li> <li>The fitting results are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4C: <ul> <li>The number matrices for the waveform shown in Figure 4C are contained in the file Figure 4.xlsx</li> <li>The original calcium imaging movie as .avi file: DLM6_Imaging.avi</li> <li>The original calcium imaging movie as tiff stack: file DLM6_imaging_tiff</li> <li>The fitting results are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4E: <ul> <li>The number matrices for the waveforms shown in Figure 4E are contained in the file Figure 4.xlsx</li> <li>The number matrices for the waveforms that resulted in the average waveforms shown in Figure 4E are contained in the file Figure 4.xlsx</li> <li>The original data files for analyses in Figure 4E are contained as text files in the folders ShakB_oe (7 text files) and control (7 text files)</li> </ul> </li> </ul>
Increasing synchrony opposes stabilizing effects of species richness on terrestrial communities
<p><span><strong>Aim</strong>: Ecological theory has predicted that species richness should stabilize communities, with mechanisms including species synchrony and population variability determining the net impacts. While these theories have been supported empirically, results can be sensitive to taxonomic bias as studies are often focused on plants. Trophic differences between consumers and primary producers can lead to varying stabilizing effects of species richness. </span><span>Here, we compared the impact of species richness on community variability in four taxonomic groups: terrestrial birds, mammals, invertebrates, and plants.</span></p> <p><span><strong>Location</strong>: Global</span></p> <p><span><strong>Method</strong>: Using data from 6,763 time series globally (BioTIME) for four terrestrial taxa, we quantified community and population variability and species synchrony based on abundance fluctuations over time.</span></p> <p><span><strong>Results</strong>: Species richness destabilized communities through increasing synchrony and stabilized communities through reducing population variability in all taxa. Such opposing effects weakened net impacts of species richness on communities. Population variability had higher importance relative to synchrony in plant communities. In contrast, synchrony had more comparable (or even higher) importance compared to population variability in animal communities. When synchrony and population variability were not controlled, stabilizing impacts of species richness were detected in plant communities only.</span></p> <p><strong><span>M</span></strong><span><strong>ain conclusions</strong>: </span><span>Our results highlight how species richness drives stabilizing and destabilizing mechanisms simultaneously across all taxa, with strong taxonomic variation in the relative importance of these mechanisms in regulating community variability. This </span><span>questions the generality of previous findings on stabilizing impacts of species richness based on limited taxonomic coverage. Additionally, our results indicate the need to understand how the importance of stabilizing and destabilizing mechanisms differ in determining community variability across organisms and environments.</span></p>
Data for: Environmental filtering drives biodiversity‒spatial stability relationships in a large temperate forest region
<ol> <li>The assumption that greater biodiversity enhances ecosystem stability, commonly known as "portfolio effect", has attracted considerable research attention. However, the potential portfolio effects on spatial stability (the similarity of ecosystem functioning among forest communities) are still poorly examined especially at different spatial scales and under varying environmental stress conditions. Accordingly, this study investigates the biodiversity-spatial stability relationship among regional communities across different spatial scales and environmental conditions in a temperate forest region.</li> <li>We define spatial stability as the invariability of the productivity of woody plants among plots within a regional community. To test spatial stability, the N closest plots to a given plot were aggregated to form regional communities representing different spatial scales. Structural equation modeling was used to evaluate how biodiversity (including taxonomic (TD) and phylogenetic diversity (PD)) increases spatial stability via species asynchrony and/or population stability across spatial scales. Hierarchical Bayesian modeling was used to evaluate the environmental dependence of the portfolio effects on spatial stability.</li> <li>TD and PD both increased spatial stability by increasing asynchrony, but decreased population stability. The portfolio effect of TD on spatial stability became stronger and reached saturation at the intermediate scale and then decreased as regional communities became larger. The portfolio effects of TD were weaker under the stressful conditions of drought, high precipitation seasonality and high elevation but unchanged across temperature seasonality and human disturbance. PD showed no discernable effect on spatial stability and did not change across spatial scale and environmental condition.</li> <li>Our results suggest that the positive effect of biodiversity on species asynchrony overcomes the negative biodiversity effect on population stability to buffer the spatial change in productivity in diverse communities. Future research on the biodiversity–spatial stability relationship may thus benefit from incorporating different spatial scales and environmental conditions into the analysis. </li> </ol>
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.