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3,655 results for “Structural data”
Data from: How the truffle got its mate: insights from genetic structure in spontaneous and planted Mediterranean populations of Tuber melanosporum
The life cycles and dispersal of edible fungi are still poorly known, thus limiting our understanding of their evolution and domestication. The prized Tuber melanosporum produces fruitbodies (fleshy organs where meiospores mature) gathered in natural, spontaneously inoculated forests or harvested in plantations of nursery-inoculated trees. Yet, how fruitbodies are formed remains unclear, thus limiting yields, and how current domestication attempts affect population genetic structure is overlooked. Fruitbodies result from mating between two haploid individuals: the maternal parent forms the flesh and the meiospores, while the paternal parent only contributes to the meiospores. We analyzed the genetic diversity of T. melanosporum comparatively in spontaneous forests versus plantations, using SSR polymorphism of 950 samples from South-East France. All populations displayed strong genetic isolation by distance at the metric scale, possibly due to animal dispersal, meiospore persistence in soil, and/or exclusion of unrelated individuals by vegetative incompatibility. High inbreeding was consistently found, suggesting that parents often develop from meiospores produced by the same fruitbody. Unlike maternal genotypes, paternal mycelia contributed to few fruitbodies each, did not persist over years, and were undetectable on tree mycorrhizae. Thus, we postulate that germlings from the soil spore bank act as paternal partners. Paternal genetic diversity and outbreeding were higher in plantations than in spontaneous truffle-grounds, perhaps because truffle growers disperse fruitbodies to maintain inoculation in plantations. However, planted and spontaneous populations were not genetically isolated, so that T. melanosporum illustrates an early step of domestication where genetic structure remains little affected.
Data from: The role of selection in driving landscape genomic structure of the waterflea Daphnia magna
The combined analysis of neutral and adaptive genetic variation is crucial to reconstruct the processes driving population genetic structure of natural populations. However, such combined analysis is challenging because of the complex interaction among neutral and selective processes in the landscape. Overcoming this level of complexity requires an unbiased search for the evidence of selection in the genomes of populations sampled from their natural habitats and the identification of demographic processes that lead to present-day populations genetic structure. Ecological model species with a suite of genomic tools and well-understood ecologies are best suited to resolve this complexity and elucidate the role of selective and demographic processes in the landscape genomic structure of natural populations. Here we investigate the water flea Daphnia magna, an emerging model system in genomics and a renowned ecological model system. We infer past and recent demographic processes by contrasting patterns of local and regional neutral genetic diversity at markers with different mutation rates. We assess the role of the environment in driving genetic variation in our study system by identifying correlates between biotic and abiotic variables naturally occurring in the landscape and patterns of neutral and adaptive genetic variation. Our results indicate that selection plays a major role in determining the population genomic structure of D. magna. First, environmental selection directly impacts genetic variation at loci hitchhiking with genes under selection. Secondly, priority effects enhanced by local genetic adaptation (cf. monopolization) affect neutral genetic variation by reducing gene flow among populations and genetic diversity within populations.
Data from: Contrasting definitive hosts as determinants of the genetic structure in a parasite with complex life cycle along the Southeastern Pacific
The spatial genetic structure (and gene flow) of parasites with complex life cycles, such as digeneans, has been attributed mainly to the dispersion ability of the most mobile host, which most often corresponds to the definitive host (DH). In this study, we compared the genetic structure and diversity of adult Neolebouria georgenascimentoi in two fish species (DHs) that are extensively distributed along the Southeastern Pacific (SEP). The analysis was based on the cytochrome oxidase subunit I gene sequences of parasites collected between 23°S and 45°S. In total, 202 sequences of N. georgenascimentoi in Pinguipes chilensis isolated from 9 sites and 136 sequences of Prolatilus jugularis from 5 sites were analyzed. Our results showed that N. georgenascimentoi is a species complex that includes three different parasite species; however, in this study, only group 1 and 2 found in P. chilensis and P. jugularis, respectively, were studied because they are widely distributed along the coastline. Group 1 parasites had two common haplotypes with wide distribution and unique haplotypes in northern sites. Group 2 had only one common haplotype with wide distribution and a large number of unique haplotypes with greater genetic diversity. Both groups have experienced recent population expansion. Only group 1 exhibited a genetic structure that was mainly associated with a biogeographic break at approximately 30°S along the SEP. Our finding suggests that host access to different prey (=intermediate hosts) could affect the genetic structure of the parasite complex discovered here. Consequently, difference between these patterns suggests that factors other than DH dispersal are involved in the genetic structure of autogenic parasites.
Data from: Spatial heterogeneity in landscape structure influences dispersal and genetic structure: empirical evidence from a grasshopper in an agricultural landscape
Dispersal may be strongly influenced by landscape and habitat characteristics that could either enhance or restrict movements of organisms. Therefore, spatial heterogeneity in landscape structure could influence gene flow and the spatial structure of populations. In the past decades, agricultural intensification has led to the reduction in grassland surfaces, their fragmentation and intensification. As these changes are not homogeneously distributed in landscapes, they have resulted in spatial heterogeneity with generally less intensified hedged farmland areas remaining alongside streams and rivers. In this study, we assessed spatial pattern of abundance and population genetic structure of a flightless grasshopper species, Pezotettix giornae, based on the surveys of 363 grasslands in a 430-km² agricultural landscape of western France. Data were analysed using geostatistics and landscape genetics based on microsatellites markers and computer simulations. Results suggested that small-scale intense dispersal allows this species to survive in intensive agricultural landscapes. A complex spatial genetic structure related to landscape and habitat characteristics was also detected. Two P. giornae genetic clusters bisected by a linear hedged farmland were inferred from clustering analyses. This linear hedged farmland was characterized by high hedgerow and grassland density as well as higher grassland temporal stability that were suspected to slow down dispersal. Computer simulations demonstrated that a linear-shaped landscape feature limiting dispersal could be detected as a barrier to gene flow and generate the observed genetic pattern. This study illustrates the relevance of using computer simulations to test hypotheses in landscape genetics studies.
Data from: Genetic differentiation and phylogeographical structure of the Brachionus calyciflorus complex in eastern China
Spatio-temporal patterns and processes of genetic differentiation in passively dispersing zooplankton are drawing much attention from both ecologists and evolutionary biologists. Two opposite phylogeographical scenarios have already been demonstrated in rotifers, which consist of high levels of genetic differentiation among populations even on small geographical scales on the one hand, and the traditionally known cosmopolitanism that is associated with high levels of gene flow and long-distance dispersal via diapausing stages on the other hand. Here we analyzed the population genetic structure and the phylogeography of the Brachionus calyciflorus species complex in eastern China. By screening a total of 318 individuals from ten locations along a 2320 km gradient and analyzing samples from two growing seasons, we aimed at focusing on both small- and large-scale patterns. We identified eight cryptic species, and verified species status of two of these by sexual reproduction tests. Samples in summer and winter yielded different cryptic species. The distribution patterns of these genetically distinct cryptic species were diverse across eastern China, from full cosmopolitanism to local endemism. The two most abundant cryptic species BcWIII and BcSW both showed a pattern of strong genetic differentiation among populations and no significant isolation-by-distance. Long-distance colonization, secondary contact and recent range expansion are probably responsible for the indistinct pattern of isolation-by-distance. Our results suggest that geographical distance is more important than temporal segregation across seasons in explaining population differentiation and the occurrence of cryptic species. We explain the current phylogeographical structure in the B. calyciflorus species complex by a combination of recent population expansion, restricted gene flow, priority effects and long-distance colonization.
Data from: Plant geographic origin and phylogeny as potential drivers of community structure in root-inhabiting fungi
1. Root-inhabiting fungal communities, including mutualists and antagonists, influence host plant performance, and can potentially shape plant community composition. However, there is uncertainty about how root-inhabiting fungal communities are structured, and if fungal community characteristics are significant predictors of host plant abundance. 2. In this study, we first assessed how root-inhabiting fungal communities were structured in relation to the phylogeny and geographic origins (native vs exotic) of their host plants in an old-field community. In addition, we took into consideration the spatial arrangements (i.e. physical locations) of the individual host plants. We then tested if the relative abundances of pathogenic and beneficial arbuscular mycorrhizal (AM) fungi could predict host plant abundances. 3. We found that host plant phylogeny was an important factor in structuring the whole fungal community, irrespective of host plant origin. Furthermore, the spatial arrangements of individual host plants were a strong predictor of AM fungal community structure. Host plant phylogeny and spatial arrangements appeared to similarly affect the structure of pathogenic fungal communities. No distinct differences were observed between native and exotic plant species in fungal community characteristics. The relative abundances of AM and pathogenic fungi were not significant predictors for observed abundances of their host plants. 4. Synthesis. Host plant phylogeny and spatial arrangements can structure naturally occurring root-inhabiting fungal communities. The absence of distinct differences in fungal community composition, including pathogens, in exotic and native plants suggests long residence times and the consequent naturalization of exotic species in the region, allowing for the establishment of similar plant-microbial interactions between native and exotic species.
Data from: Genetic diversity and population structure of Trypanosoma brucei in Uganda: implications for the epidemiology of sleeping sickness and Nagana
Background: While Human African Trypanosomiasis (HAT) is in decline on the continent of Africa, the disease still remains a major health problem in Uganda. There are recurrent sporadic outbreaks in the traditionally endemic areas in south-east Uganda, and continued spread to new unaffected areas in central Uganda. We evaluated the evolutionary dynamics underpinning the origin of new foci and the impact of host species on parasite genetic diversity in Uganda. We genotyped 269 Trypanosoma brucei isolates collected from different regions in Uganda and southwestern Kenya at 17 microsatellite loci, and checked for the presence of the SRA gene that confers human infectivity to T. b. rhodesiense. Results: Both Bayesian clustering methods and Discriminant Analysis of Principal Components partition Trypanosoma brucei isolates obtained from Uganda and southwestern Kenya into three distinct genetic clusters. Clusters 1 and 3 include isolates from central and southern Uganda, while cluster 2 contains mostly isolates from southwestern Kenya. These three clusters are not sorted by subspecies designation (T. b. brucei vs T. b. rhodesiense), host or date of collection. The analyses also show evidence of genetic admixture among the three genetic clusters and long-range dispersal, suggesting recent and possibly on-going gene flow between them. Conclusions: Our results show that the expansion of the disease to the new foci in central Uganda occurred from the northward spread of T. b. rhodesiense (Tbr). They also confirm the emergence of the human infective strains (Tbr) from non-infective T. b. brucei (Tbb) strains of different genetic backgrounds, and the importance of cattle as Tbr reservoir, as confounders that shape the epidemiology of sleeping sickness in the region.
Data from: The mutational structure of metabolism in Caenorhabditis elegans
A properly functioning organism must maintain metabolic homeostasis. Deleterious mutations degrade organismal function, presumably at least in part via effects on metabolic function. Here we present an initial investigation into the mutational structure of the Caenorhabditis elegans metabolome by means of a mutation accumulation experiment. We find that pool sizes of 29 metabolites vary greatly in their vulnerability to mutation, both in terms of the rate of accumulation of genetic variance (the mutational variance, VM) and the rate of change of the trait mean (the mutational bias, ΔM). Strikingly, some metabolites are much more vulnerable to mutation than any other trait previously studied in the same way. Although we cannot statistically assess the strength of mutational correlations between individual metabolites, principal component analysis provides strong evidence that some metabolite pools are genetically correlated, but also that there is substantial scope for independent evolution of different groups of metabolites. Averaged over mutation accumulation lines, PC3 is positively correlated with relative fitness, but a model in which metabolites are uncorrelated with fitness is nearly as good by Akaike's Information Criterion.
Data from: Fallow management increases habitat suitability for endangered steppe bird species through changes in vegetation structure
In the face of the dramatic worldwide decline of farmland bird populations, the preservation of fallow fields is a conservation measure encouraged through subsidies (e.g. agri‐environmental schemes, AES). Beyond the general benefits of increasing fallow availability for endangered steppe bird populations, there is a lack of knowledge on how fallow management can contribute to meeting species‐specific habitat requirements. We used occurrence data from three steppe bird species protected at the EU level (Stone Curlew Burhinus oedicnemus, Little Bustard Tetrax tetrax, and Calandra Lark Melanocorypha calandra), framed in a quasi‐experimental approach covering an unprecedented spatio‐temporal scale that included 612 fallow fields over a three‐year study period in an agricultural Mediterranean landscape (Spain). We used path analysis to explore the mechanisms by which common agricultural practices affected species‐specific occurrence. We examined partial effects of agricultural practices on vegetation structure and food availability, and the partial effect of these variables on bird occurrence compared to control fields (no agricultural practices applied). Agricultural practices had a significant effect on the presence of the three studied species. Through changes in the vegetation structure, Shredding + Herbicide and Tillage increased the occurrence of the Stone Curlew and Shredding increased the occurrence of the Little Bustard. The occurrence of Calandra Lark was mostly affected by landscape variables. Synthesis and applications. Our study highlights that, in addition to the acknowledged positive role of fallow availability, applying a limited number of specific agricultural practices before the breeding season can further increase bird occurrence by changing the vegetation structure. Using path analysis, we explored the mechanisms driving the occurrence of three steppe bird species under different agricultural practices. Such information is key to providing specific recommendations for future conservation management of endangered species within agri‐environmental schemes.
Simulation results and lambda value data for structural connectome based simulations of temporal lobe epilepsy surgery.
<p>This data file contains a number of matlab matrices holding the results of simulations carried out using structural connectome data from healthy individuals and individuals with a diagnosis of temporal lobe epilepsy (TLE). The results are in the form of either time values, representing the time at which brain regions in the simulations 'escaped' into a seizure state, or corresponding node labels which represent the region that escaped at that time. Simulations were stopped after the first three nodes escaped, and then repeated over 100 iterations. There were 39 controls and 22 left TLE patients. Simulations were also carried out for altered structural connectomes simulating surgery influence on the time taken for nodes to escape. Clinical resection (clinres), subject-specific resections (subspecres) or random resections (ranres). 'Lmdas' shows the deviation from the control average of surface areas for each region in each subject, normalised to lie between 0 and 1. 'Names' is a cell array containing the node labels for the 82 regions.</p>
FIGURES 17–26. Female genital structures. Female sternite 8, 17 in Hypera kayali sp. nov. (Coleoptera: Curculionidae, Hyperini) from Syria, with bionomic data
FIGURES 17–26. Female genital structures. Female sternite 8, 17, Hypera (Dapalinus) kayali sp. nov.; 18, H. contaminata; 19, H. dapalis; 20, H. striata; 21, H. subvittata, all dorsal view. Female genitalia, spermatheca, 22, H. kayali sp. nov.; 23, H. contaminata; 24, H. dapalis; 25, H. striata; 26, H. subvittata, all lateral view. Scale bar 0.1 mm.
FIGURES 1–9. 1–3. Philophylla millei, n in A new species of Philophylla Rondani (Diptera: Tephritidae: Trypetini) from New Caledonia, recognized based on female postabdominal structure and molecular sequence data
FIGURES 1–9. 1–3. Philophylla millei, n. sp., holotype male. 4. P. millei, n. sp., paratype female. 5. Anastrephoides matsumurai Shiraki, female. 6. Myoleja korneyevi Han & Kütük, female. 7. M. korneyevi, male. 8. Anastrepha barnesi Aldrich. 9. Anastrepha obliqua (Macquart). Abbreviations: AAB = anterior apical band; PAB = posterior apical band; RMB = radial-medial band; SAB = subapical band; SCB = subcostal band.
FIGURE 16 in A new species of Philophylla Rondani (Diptera: Tephritidae: Trypetini) from New Caledonia, recognized based on female postabdominal structure and molecular sequence data
FIGURE 16. Relationships of the tribe Trypetini inferred from neighbor-joining tree based on Kimura two parameter distances (1159 bp after gaps and sites with missing data removed). The first number is the Pc value from the standard error test (higher than 90%), and the second number is the Pb from the bootstrap test (2000 replications).
FIGURE 10–15 in A new species of Philophylla Rondani (Diptera: Tephritidae: Trypetini) from New Caledonia, recognized based on female postabdominal structure and molecular sequence data
FIGURE 10–15. Philophylla millei, sp.n.; 10. epandrium and surstyli, posterior view (proctiger removed); 11. epandrium, surstyli, and proctiger, lateral view; 12. glans, dorsolateral view (inset at 8x main figure); 13. female postabdomen, ventral and dorsal views (insets at 8x main figures); 14. spermatheca; 15. aculeus, ventral and dorsolateral views.
Data for model analysis in "Beyond the growth rate of cosmic structure: Testing modified gravity models with an extra degree of freedom", arXiv:1502.03710
<p>SQLite databases containing theoretical predictions for the model comparison in arXiv:1502:03710.</p>
Data for: Sequence and structural diversity of mouse Y chromosomes
<p>SNV and small indel variant calls in VCF format; sample manifest as Excel spreadsheet.</p>
Data analysis scripts for Marsh et al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'
<p>Data analysis scripts for the manuscript <strong>Marsh<em> </em>et<em> </em>al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'</strong></p> <p><strong>Update for Version 2:</strong> The calculation of confidence intervals around the mean effects in Figure 2 has been updated to use the <code>marginaleffects</code> package (many thanks to Biao Wang and Shuang Zhang for pointing out an error in the original code). Using the Satterthwaite method for determining degrees of freedom, the updated confidence intervals are around 32% smaller than our original estimates (MLF = 32.0%, HLF = 32.1%, OP = 21.6%). Note, this change is only relevant to fig. 2 and figs. S2-4; the mean effect sizes and trends along the disturbance gradient, all statistical comparisons, and the constrast analyses in fig. 3 remain unaffected. The updated figures S2-4 and Table S6 can be seen in the file 'Updated figures S2-4 with recalculated confidence intervals.pdf'.</p> <p>In the zip file 'BALI_synthesis_analysis.zip' there are outputs from RMarkdown scripts that include all steps of the analysis for each dataset, including R code, incorporating data visualisation, exploration and standardisation, model building and evaluation, and visualisation of results. Fig. 2b can be regenerated using code in the zip file 'Marsh_etal_2024_Science_fig1b_chm_and_canopy_profiles-main.zip'.</p> <p>Each dataset presented in the manuscript has an html file within the folder 'Analyses'. For datasets involving bat, bird, dung beetle and tree traits additional markdown documents are available for steps take during data preparation in the folder 'Data preparation'.</p> <p>In the zip file 'BALI_synthesis_data.zip' are .rds data files that have been cleaned, prepared and z-score standardised following the procedures outlined in the respective markdown files.</p> <p>To repeat any given analysis, follow the respective rmarkdown document, excluding the data manipulation steps:</p> <ol> <li>Read in the data file as described above: dd <- readRDS(paste0("path/to/rds/file/", "name_of_file.rds"))</li> <li>Run the code at the top of the markdown workflow (sections "Data information" and "Load in necessary libraries")</li> <li>Do not run the sections "Read in data" through to "Visual inspection of the data"</li> <li>Continue the analysis from the 'Modelling' section</li> </ol> <div> <h3> </h3> <h3>Level 1 - Structure & Environment</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Above-ground carbon</td> <td>Above ground carbon</td> <td>Above_ground_carbon</td> <td>Terhi Riutta</td> </tr> <tr> <td>Leaf-area index</td> <td>Leaf-area index</td> <td>Leaf_area_index</td> <td>Terhi Riutta</td> </tr> <tr> <td>Soil temperature</td> <td>Soil temp.</td> <td>Soil_temperature</td> <td>Terhi Riutta</td> </tr> <tr> <td>Soil moisture</td> <td>Soil moisture</td> <td>Soil_moisture</td> <td>Dafydd Elias</td> </tr> <tr> <td>Air temperature: Minimum</td> <td>Air temp.: Min.</td> <td>Air_temperature_minimum</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Air temperature: Mean</td> <td>Air temp.: Mean</td> <td>Air_temperature_mean</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Air temperature: Maximum</td> <td>Air temp.: Max.</td> <td>Air_temperature_maximum</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Soil bulk density</td> <td>Soil bulk density</td> <td>Soil_bulk_density</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil horizon depth</td> <td>Soil horizon depth</td> <td>Soil_horizon_depth</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil pH</td> <td>Soil pH</td> <td>Soil_pH</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon</td> <td>Soil nutrients (C)</td> <td>Soil_nutrients_C</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Nitrogen</td> <td>Soil nutrients (N)</td> <td>Soil_nutrients_N</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Inorganic Phosporous</td> <td>Soil nutrients (Inorganic P)</td> <td>Soil_nutrients_Inorganic_P</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon:Phosphorous</td> <td>Soil nutrients (C:P)</td> <td>Soil_nutrients_C_P</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon:Nitrogen</td> <td>Soil nutrients (C:N)</td> <td>Soil_nutrients_C_N</td> <td>Dafydd Elias</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 2 - Tree traits</h3> </div> <p>All tree traits were collected as part of the following study (details in this table have been extracted from table S1 of that publication): S. Both, T. Riutta, C.E.T. Paine, D.M.O. Elias, R.S. Cruz, A. Jain, D. Johnson, U.H. Kritzler, M. Kuntz, N. Majalap-Lee, N. Mielke, M.X. Montoya Pillco, N.J. Ostle, Y. Arn Teh, Y. Malhi, D.F.R.P. Burslem (2019) Logging and soil nutrients independently explain plant trait expression in tropical forests. New Phytologist. 221:4, 1853–1865.</p> <p> </p> <p><em><strong>Photosynthesis Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated photosynthesis traits</td> <td>Photosyn. traits</td> <td>Photosynthesis traits</td> </tr> <tr> <td>δ<sup>13</sup>C</td> <td>δ<sup>13</sup>C</td> <td>Traits_13C</td> </tr> <tr> <td>Light-saturated photosynthetic rate</td> <td>Photosyn. rate: A<sub>sat</sub></td> <td>Traits_Asat</td> </tr> <tr> <td>Maximum photosynthetic rate</td> <td>Photosyn. rate: A<sub>max</sub></td> <td>Traits_Amax</td> </tr> <tr> <td>Maximum photosynthetic rate: Nitrogen concentration</td> <td>Max. photosyn. rate: N(%)</td> <td>Traits_N_conc</td> </tr> <tr> <td>Maximum photosynthetic rate: Phosphorous mass (area)</td> <td>Max. photosyn. rate: P(mass)</td> <td>Traits_Phos_area</td> </tr> <tr> <td>Dark respiration (Rd)</td> <td>Dark respiration</td> <td>Traits_Dark_resp</td> </tr> <tr> <td>Specific leaf area (SLA)</td> <td>Specific leaf area</td> <td>Traits_SLA</td> </tr> <tr> <td>Carotenoids (area)</td> <td>Carotenoids: Area</td> <td>Traits_Carot_area</td> </tr> <tr> <td>Carotenoids (mass)</td> <td>Carotenoids: Mass</td> <td>Traits_Carot_mass</td> </tr> <tr> <td>Chlorophyll a (area)</td> <td>Chlorophyll a: Area</td> <td>Traits_Chl_a_area</td> </tr> <tr> <td>Chlorophyll a (mass)</td> <td>Chlorophyll a: Mass</td> <td>Traits_Chl_a_mass</td> </tr> <tr> <td>Chlorophyll b (area)</td> <td>Chlorophyll b: Area</td> <td>Traits_Chl_b_area</td> </tr> <tr> <td>Chlorophyll b (mass)</td> <td>Chlorophyll b: Mass</td> <td>Traits_Chl_b_mass</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Nutrient Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated nutrient traits</td> <td>Nutrient traits</td> <td>Nutrient_traits</td> </tr> <tr> <td>δ<sup>15</sup>N</td> <td>δ<sup>15</sup>N</td> <td>Traits_15N</td> </tr> <tr> <td>Carbon concentration</td> <td>Carbon conc.</td> <td>Traits_Carbon_conc</td> </tr> <tr> <td>Nitrogen concentration</td> <td>Max. photosyn. rate: N(%)</td> <td>Traits_N_perc</td> </tr> <tr> <td>Phosphorous concentration</td> <td>Max. photosyn. rate: P(mass)</td> <td>Traits_Phos_mass</td> </tr> <tr> <td>Magnesium concentration</td> <td>Regulat. nutrients: Total Mg</td> <td>Traits_Total_Mg</td> </tr> <tr> <td>Potassium concentration</td> <td>Regulat. nutrients: Total K</td> <td>Traits_Total_K</td> </tr> <tr> <td>Calcium concentration</td> <td>Regulat. nutrients: Total Ca</td> <td>Traits_Total_Ca</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Structural Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated structural traits</td> <td>Structural traits</td> <td>Structural_traits</td> </tr> <tr> <td>Branch specific density</td> <td>Branch wood density</td> <td>Traits_Branch_WD</td> </tr> <tr> <td>Leaf cellulose concentration</td> <td>Leaf fibre conc.: Cellul.</td> <td>Traits_Cellulose</td> </tr> <tr> <td>Leaf lignin concentration</td> <td>Leaf fibre conc.: Lignin</td> <td>Traits_Lignin</td> </tr> <tr> <td>Leaf hemicellulose concentration</td> <td>Leaf fibre conc.: Hemicel.</td> <td>Traits_Hemicellulose</td> </tr> <tr> <td>Leaf area</td> <td>Leaf size: Area</td> <td>Traits_Leaf_area</td> </tr> <tr> <td>Leaf dry weight</td> <td>Leaf size: Dry wgt</td> <td>Traits_Dry_weight</td> </tr> <tr> <td>Leaf force to punch</td> <td>Leaf strength: Tough.</td> <td>Traits_Leaf_toughness</td> </tr> <tr> <td>Leaf thickness</td> <td>Leaf strength: Thick.</td> <td>Traits_Leaf_thickness</td> </tr> <tr> <td>Leaf dry matter content</td> <td>Leaf strength: Dry mat.</td> <td>Traits_LDMC</td> </tr> <tr> <td>Total phenol concentration</td> <td>Leaf defence: Phenol</td> <td>Traits_Phenol</td> </tr> <tr> <td>Total tannin concentration</td> <td>Leaf defenct: Tannin</td> <td>Traits_Tannin</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 3 - Biodiversity</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Soil bacterial richness</td> <td>Soil microbial richness: Bacteria</td> <td>Soil_richness_Bacteria</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil protist richness</td> <td>Soil microbial richness: Protists</td> <td>Soil_richness_Protist</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil ectomycorrhizal richness</td> <td>Soil fungal richness: Ectomycorrhiza</td> <td>Soil_richness_Ectomycorrhiza</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil fungal richness</td> <td>Soil fungal richness: Fungi</td> <td>Soil_richness_Fungi</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil arbuscular mycorrhizal richness</td> <td>Soil fungal richness: Arbuscular mycorrhiza</td> <td>Soil_richness_Arbuscular_mycorrhizal</td> <td>Dafydd Elias</td> </tr> <tr> <td>Leaf spectral diversity</td> <td>Spectral diversity</td> <td>Spectral_diversity</td> <td>Matheus Nunes</td> </tr> <tr> <td>Liana abundance</td> <td>Liana abundance</td> <td>Liana_abundance</td> <td>Boris Bongalov</td> </tr> <tr> <td>Dung beetle abundance</td> <td>Dung beetle abund.</td> <td>Dung_beetle_abundance</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: richness</td> <td>Dung beetle diversity: q=0</td> <td>Dung_beetle_diversity_q=0</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: Shannon diversity</td> <td>Dung beetle diversity: q=1</td> <td>Dung_beetle_diversity_q=1</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: Simpson diversity</td> <td>Dung beetle diversity: q=2</td> <td>Dung_beetle_diversity_q=2</td> <td>Eleanor Slade</td> </tr> <tr> <td>Bird abundance</td> <td>Bird abund.</td> <td>Bird_abundance</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: richness</td> <td>Bird diversity: q=0</td> <td>Bird_diversity_q=0</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: Shannon diversity</td> <td>Bird diversity: q=1</td> <td>Bird_diversity_q=1</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: Simpsons diversity</td> <td>Bird diversity: q=2</td> <td>Bird_diversity_q=2</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bat abundance</td> <td>Bat abund.</td> <td>Bat_abundance</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (small scale)</td> <td>Bat diversity (sm scale)</td> <td>Bat_diversity_small_scale</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): richness</td> <td>Bat diversity (lg scale): q=0</td> <td>Bat_diversity_large_scale_q=0</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): Shannon diversity</td> <td>Bat diversity (lg scale): q=1</td> <td>Bat_diversity_large_scale_q=1</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): Simpson diversity</td> <td>Bat diversity (lg scale): q=2</td> <td>Bat_diversity_large_scale_q=2</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Nestedness</td> <td>Bat β-diversity: Nested.</td> <td>Bat_beta_diversity_Nestedness</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Turnover</td> <td>Bat β-diversity: Turn.</td> <td>Bat_beta_diversity_Turnover</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Total</td> <td>Bat β-diversity: Total</td> <td>Bat_beta_diversity_Total</td> <td>David Hemprich-Bennett</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 4 - Functioning</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Soil respiration</td> <td>Respiration: Soil</td> <td>Soil_respiration</td> <td>Terhi Riutta</td> </tr> <tr> <td>Stem respiration</td> <td>Respiration: Stem</td> <td>Stem_respiration</td> <td>Terhi Riutta</td> </tr> <tr> <td>Net primary productivity</td> <td>NPP</td> <td>NPP</td> <td>Terhi Riutta</td> </tr> <tr> <td>Litterfall</td> <td>Litterfall</td> <td>Litterfall</td> <td>Terhi Riutta</td> </tr> <tr> <td>Leaf litter decomposition</td> <td>Litter decomposition</td> <td>Litter_decomposition</td> <td>Sabine Both</td> </tr> <tr> <td>Soil mycelial production</td> <td>Mycelial production</td> <td>Hyphal_length</td> <td>Samuel Robinson</td> </tr> <tr> <td>Dung removal</td> <td>Dung removal</td> <td>Dung_removal</td> <td>Eleanor Slade</td> </tr> </tbody> </table> <p> </p> <h2>Funding</h2> <p>Analyses were carried out, and data were collected, as part of the BALI (Biodiversity And Land-use Impacts on tropical ecosystem function) and LOMBOK (Land-use Options for Maintaining BiOdiversity & eKosystem functions) projects using the following funding:</p> <ul> <li>NERC Human-modified Tropical Forests Programme (NE/K016377/1, NE/K016261/1, NE/K016148/1, NE/K016407/1);</li> <li>NERC grant (NE/I028068/1);</li> <li>British Ecological Society Small Ecological Project Grant (No.: 3256/4035);</li> <li>Varley-Gradwell Travelling Fellowship in Insect Ecology;</li> <li>Bat Conservation International Student Research Scholarship;</li> <li>NOMIS Foundation;</li> <li>ERC European Union's Horizon 2020 research and innovation programme (grant agreement No 865403);</li> <li>ERC Advanced Investigator Grant, GEM-TRAIT (321131);</li> <li>The SAFE Project is funded by the Sime Darby Foundation.</li> </ul>
Supplemental data for Structural dynamics of therapeutic nucleic acids with phosphorothioate backbone modification, Carlesso et al.
<p>Description of the parameterization strategy for phosphorothioate-modified therapeutic nucleic acids, and files related to the system preparation, MD trajectories and analysis.</p>
AFM data of ice clusters on Cu(111) and Au(111) in paper "Structure discovery in Atomic Force Microscopy imaging of ice"
<p>Frequency shift CO-tip atomic force microscopy data of small ice clusters on Cu(111) and Au(111) surfaces as they appear in the paper "Structure discovery in Atomic Force Microscopy imaging of ice".</p><p>The data are saved in a compressed .tar.gz archive. The unpacked archive contains each experiment as a Numpy .npz file. Each file contains the measurement data as a 3D array in the key 'data' and the physical extent of the scan region in the x and y directions in Ånströms in the keys 'lengthX' and 'lengthY'.</p>
Structural Data for the Antibody Developability Manuscript: Cartography of Developability Landscapes in Native and Human-Engineered Antibodies
<p>Here is the additional structural data (predicted models and MD trajectories) for the "Cartography of the Developability Landscapes of Native and Human-Engineered Antibodies" manuscript. Please cite our paper when referring to and using this data. If you have any questions, please contact Eva Smorodina at ribes.ev@gmail.com. Thank you!</p>
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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.