Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

Raw and processed GO term data to support running GCEA analyses using ensemble-based nulls, as described in the manuscript, 'Overcoming bias in gene category enrichment analyses of brain-wide transcriptomic data'.

Data to support a toolbox for performing gene category enrichment analyses, including against ensembles of null phenotypes. Descriptions of how these data files can be used for this purpose are in the documentation for the toolbox, at https://github.com/benfulcher/GCEA_FalsePositives

opencc-by-4.0Sep 2021View details →

Dataset related to article "Molecular Studies and ex vivo Complement assay on Endothelium Highlight the Genetic Complexity of Atypical Hemolytic Uremic Syndrome: The Case of a Pedigree With a Null CD46 Variant".

The files contain raw data related to the article "Molecular Studies and ex vivo Complement assay on Endothelium Highlight the Genetic Complexity of Atypical Hemolytic Uremic Syndrome: The Case of a Pedigree With a Null CD46 Variant", available from <a href="https://www.frontiersin.org/articles/10.3389/fmed.2020.579418/full">https://www.frontiersin.org/articles/10.3389/fmed.2020.579418/ful</a>l. File "Genetic and clinical data": <ul> <li>In the sheet "485 aHUS patients" are reported data obtained from the screening of 485 unrelated patients with aHUS including rare variants (RVs) in complement disease-associated genes (CFH, CD46, CFI, C3, CFB and THBD), the presence of CFH-CFHR genomic rearrangements and/or anti-FH antibodies.</li> <li>In the sheet "Pedigrees with c.286+2T>G" are listed all pedigrees carrying the c.286+2T>G variant, the diseases status of all subjects and the age of disease onset of patients. In bold are indicated pedigrees (n=7) used to study the penetrance of aHUS in c.286+2T>G carriers.</li> <li>In the sheet "Haplotypes" are reported genotypes used to evaluate the association between the presence of CFH-H3 and CD46GGAAC risk haplotypes and aHUS. Results of this analysis are reported in Table 3 of the published paper.</li> <li>In the sheet "Raw data Fig.2" are reported data of "platelet count" and "serum creatinine" of the proband used to elaborate Figure 2.</li> </ul> In the file "C3 and C5b-9 deposition" is reported the quantification of serum-induced C3 and C5b-9 deposition on human microvascular endothelial cell line (HMEC-1). The fluorescent staining was evaluated with Image J and expressed as pixel2 per field analyzed. The fields with the lowest and highest values were excluded from calculation. These values were used to elaborate data included in Table 2 and in Figure 5. In the file "CD46 protein expression" are reported data of CD46 expression on peripheral blood mononuclear cells (PBMCs) isolated from the proband, his relatives and healthy volunteers. Data of specific expression of CD46 (evaluated for SCR1 or for SCR4 as reported in the materials and methods section) are indicated as median fluorescence intensity (MFI) percentage compared with the control. In the ppt file "cDNA amplification and sequencing results" is reported: <ul> <li>the agarose gel image of the amplified cDNA from the control (ctr), the proband (IV-8) and his healthy father (III-7).</li> <li>Electropherograms obtained from the cDNA sequencing of the control (ctr), the proband (IV-8) and his healthy father (III-7).</li> </ul> Additional data will be made available by the authors, without undue reservation, to any qualified researcher.

opencc-by-4.0Mar 2023View details →

Centripetal migration in Drosophila ovary IX: E-cadherin null clones pt2 & E-Cadherin germ cell RNAi pt 2

Part of data supporting Figs 6, 7, S3, S6, S16 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492 Data file descriptions: <ul> <li>“FRT G13 mitotic clones”                                               14.6 GB</li> </ul>             Fixed sample image data for clones of cells with E-Cadherin mutant or control mitotic clones <ul> <li>“GC RNAi flipout timelapse data pt2”                          28.92GB</li> </ul>             Timelapse image data for clones of germ cells with E-Cadherin knockdown <ul> <li> “Image analysis of ring canals-fixed G13 control”        6 KB</li> </ul>             Evaluation of fixed samples for ring canal position just prior to stage 11, nurse cell dumping, using <ul> <li>“Immuno Shg LOF clonal analysis”                                     98 KB</li> </ul>             Preliminary evaluation of sample image with clones of cells with E-Cadherin mutant or  control mitotic clones <ul> <li>“Live GC RNAi clonal data Prelim Eval”                                 25.2 MB</li> </ul>             Preliminary evaluation of germ cell E-Cadherin knockdown samples

opencc-by-4.0Mar 2023View details →

Centripetal migration in Drosophila ovary VIII: E-cadherin null mitotic clones pt1

Data supporting Figs. 6, 7, S6 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492 <ul> <li>“Immuno Shg LOF clonal analysis”                                    98 KB</li> </ul>             Preliminary evaluation of sample image with clones of cells with E-Cadherin mutant or control mitotic clones <ul> <li>“Mixed control and shg mutant mitotic clones”       44.1Gb </li> </ul>             Fixed sample image data for clones of cells with E-Cadherin mutant or control mitotic clones

opencc-by-4.0Nov 2020View details →

Data and code for the manuscript: "Varying richness need not imply non-random species co-occurrence: implications for specifying null models"

Data and R code for the manuscript "Varying richness need not imply non-random species co-occurrence: implications for specifying null models".

opencc-by-4.0Dec 2023View details →

Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"

This is a companion dataset to the manuscript:  Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard. This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion. The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value. The *.csv file contains the geological data: location of the contacts and orientation data.

opencc-by-4.0Oct 2023View details →

Dataset: Maurel et al. "Hayabusa 2 returned samples reveal a weak to null magnetic field during aqueous alteration of Ryugu's parent body"

Samples: C0005 and A0154a from asteroid Ryugu (JAXA Hayabusa 2 mission), CI chondrite Orgueil, CM2 chondrite Daoura 003 <ul> <li>C0005: NRM, ARM, IRM demagnetization and anisotropy of ARM (AARM), IRM acquisition</li> <li>A0154a: NRM demagnetization, AARM</li> <li>Orgueil: NRM and ARM demagnetization</li> <li>Daoura 003: NRM demagnetization</li> </ul>

opencc-by-4.0May 2023View details →

Sources of prey availability data alter interpretation of outputs from prey choice null networks

Spider surveys Data collection was described previously by Cuff, Tercel, et al., (2022). This study pertains to a subset of those data, collected between 1st May and 9th July 2018 at 19 separate locations, for which paired sticky trap and vacuum sample data were collected (described below). Briefly, money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from webs and the ground. Transects were randomly distributed across the entire field. Along these transects, separate 4 m2 quadrats, at least 10 m apart, were searched and all observed linyphiids and lycosids were collected. Spiders were placed in 100 % ethanol using an aspirator, regularly changing meshing to limit potential cross-contamination. Linyphiids occupying webs were prioritised for collection, but ground-active spiders were also collected. Spiders were taken to Cardiff University, transferred to fresh ethanol and stored at -80 °C in 100 % ethanol until DNA extraction. Extraction, amplification and sequencing of DNA, and bioinformatic analysis is described by Cuff, Tercel, et al., (2022) and Drake et al., (2022), and is also detailed below. Extraction and high-throughput sequencing of spider gut DNA Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera Erigone, Tenuiphantes, Bathyphantes and Microlinyphia (Araneae: Linyphiidae), and the Lycosidae genus Pardosa. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key (Roberts, 1993). Abdomens were removed from spiders and again transferred to and washed in fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens (Krehenwinkel et al., 2017). For amplification of DNA, two primer pairs were used. BerenF-LuthienR (Cuff et al., 2021) amplified a broad range of invertebrates including spiders, and TelperionF-LaureR (Cuff et al., 2022), amplified a range of invertebrates but fewer spiders. Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively. Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet et al. (2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed Drake et al. (2022). Bioinformatic analysis The Illumina run generated 11,165,405 and 10,959,010 reads for BerenF-LuthienR and TelperionF-LaureR, respectively, which were quality-checked and paired via FastP (Chen et al., 2018)  to retain only sequences of at least 200 bp with a quality threshold of 33, resulting in 10,561,874 and 9,355,112 paired reads. The paired reads were demultiplexed and assigned to their respective spider sample according to their MID-tags via the “trim.seqs” command in Mothur v1.39.5 (Schloss et al., 2009), leaving 7,854,610 and 7,437,929 reads with exact matches to the primer and MID-tags. Replicates were removed, and denoising and clustering to zero-radius operational taxonomic units (ZOTUs; clustered without % identity to avoid multiple species represented within a single operational taxonomic unit (OTU)) completed via Unoise3 in Usearch11 (Edgar, 2010). The resultant sequences were assigned a taxonomic identity from GenBank via BLASTn v2.7.1 (Camacho et al., 2009) using a 97 % identity threshold (Alberdi et al., 2017). The BLAST output was analysed in MEGAN v6.15.2 (Huson et al., 2016). Where the top BLAST hit, determined by lowest e-value, was resolved at a higher taxonomic level than species-level, the results were checked; where possibly erroneous entries were preventing species-level assignment (e.g., poorly resolved identifications on GenBank), finer resolution was assigned based on the next-closest match. Where ZOTUs were assigned the same taxon, these were aggregated. Data clean-up used the optimal minimum sequence copy thresholds identified by Drake et al. (2022). The maximum value for a ZOTU present in blank or negative controls was identified and subtracted from all read counts for that ZOTU to remove background contaminants. Simultaneously, known lab contaminants (e.g., German cockroach Blattella germanica), artefacts and errors of the sequencing process, unexpected reads in positive controls and positive control taxon reads in dietary samples were identified. These were calculated as a percentage of their respective sample’s read count and any read counts lower than the highest of these percentages for their respective sample were removed to eliminate additional instances of contamination. These thresholds were defined as 0.38 % and 0.39 % for BerenF-LuthienR and TelperionF-LaureR, respectively. The data from the two libraries (i.e., from each primer pair) were then aggregated together by sample and aggregated again by taxon. Non-target taxa (e.g., fungi) and instances in which predator DNA was amplified (i.e., ZOTUs with high read counts matching the individual’s morphological identity) were removed.  The resultant sequencing read counts were converted into relative proportions (all values made to sum to one within each sample) and a mean value across the two primer pairs retained for each taxon within each sample. Relative read abundances were converted to presence-absence data of each detected prey taxon in each individual spider, but relative read abundance data were also retained for separate analyses to compare experimental outcomes between treatments. Invertebrate surveys To estimate prey availability using sticky traps, we placed one white dry 100 mm x 125 mm trap (Oecos) in the 4 m2 quadrat centred at the position where the spider was captured. The trap was suspended with wire approximately 25 mm above the ground to catch falling, crawling and flying invertebrates, and left in place for 72 hours. Invertebrates were identified on the traps under a stereomicroscope. To estimate prey availability using suction sampling, ground and crop stems were sampled using a ‘G-vac’ for approximately 30 seconds at each location. The collected material was emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab. All invertebrates were identified to family level to match the resolution of the least resolved of the metabarcoding-derived trophic interaction data, and due to difficulties associated with identification to finer taxonomic resolution for many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level, and wasps of the superfamily Ichneumonoidea which were identified no further due to obscurity of wing venation due to damage following suction sampling. Statistical Analysis All analyses were conducted in R v4.0.3 (R Core Team, 2021) and carried out on invertebrate data at the family or superfamily level. Alongside the dietary data derived from metabarcoding, and prey availability as determined directly by suction sampling (abundance) and sticky trapping (activity density), three additional datasets were generated where two were designed to combine data from the two trapping methods. The first approach simply set all invertebrate taxa detected in the field to have equal abundance, to provide a baseline against which to assess the effects of different prey abundance estimates. When generating the two combined data sets, it was apparent that simply adding them together would underrepresent one of the datasets as abundance and activity density are measured in different units. Therefore, a ‘proportional combined’ dataset was generated by converting counts to relative proportions of each sample (to equally weight the two methods), which were then combined by summing proportions between the two methods for each sample, multiplied by the total count of individuals across both methods for each sample (to create realistic abundance values), and then rounded to the nearest integer (to return count data). In addition, a ‘frequency of occurrence (FOO) combined’ dataset was generated by converting counts to binary presence-absence values of each sample, which were then summed between the two methods for each sample. To assess the diversity represented by the two sampling methods and their combinations, and the completeness of those datasets, coverage-based rarefaction and extrapolation were carried out, and Hill diversity calculated (Chao et al., 2014; Roswell et al., 2021) using the ‘iNEXT’ package with families represented by frequency-of-occurrence across samples (Chao et al., 2014; Hsieh et al., 2016). The remaining analyses were performed using both presence-absence and relative read abundance dietary data separately to show how differences in the treatment of the observed data are reflected in the outcomes of the analyses. Figures and outputs given in the main text relate to the presence-absence data, while relative read abundance figures and outputs are presented in the Supplementary Information. Prey preferences of spiders were analysed using network-based null models in the ‘econullnetr’ package (Vaughan et al., 2018) with the ‘generate_null_net’ function. Econullnetr generates null models based on prey availability to predict how consumers would forage if based on the availability of resources alone. These null models are then compared against the observed interactions of consumers (e.g., interactions of spiders with their prey based on dietary metabarcoding) to ascertain the extent to which resource consumption deviated from random. In five separate null models, prey availability was represented separately by the datasets described above: abundance (suction sampling), activity density (sticky trapping), proportional combined, FOO combined and equal prey abundance. To compare effect sizes between null models for each resource taxon, mean prey preference standardised effect size (SES) values were calculated from the individual spiders per model. The SES values were plotted and joined between taxa to visualise paired differences using ‘ggplot’ (Wickham, 2016). Null model-predicted trophic interactions were generated via an econullnetr null model with 999 simulations with outputs extended to allow the comparison of the null interactions for individual consumers (generate_null_net_indiv; Cuff, Kitson, et al., 2023). A visualisation of the per-individual differences in null model and observed data was generated via non-metric multi-dimensional scaling (NMDS) using the ‘metaMDS’ function in the ‘vegan’ package (Oksanen et al., 2016) in two dimensions and 9999 simulations, with Euclidean distance. Centroid coordinates for each null model and the observed data were extracted and pairwise distances calculated between model centroids: The ‘observed’ network (i.e., the network determined solely by dietary data, not necessarily the objectively ‘true’ network) and each null network were visualised with the associated prey choice effect sizes as a bipartite network using ‘ggnetwork’ (Briatte, 2021; Wickham, 2016) via an ‘igraph’ object (Csardi & Nepusz, 2006). The degree of each prey node, weighted nestedness and linkage density were generated using the ‘bipartite’ package (Dormann et al., 2008) for each network and compared visually via ggplot2.

dryad40/100

Supplementary data for: Comparison of phenotypic and transcriptomic profiles between HFPO-DA and prototypical PPARα, PPARγ, and cytotoxic agents in wild-type and Ppara-null mouse livers

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publicApr 2025View details →

openother-openApr 2020View details →

Three-Dimensional Bernstein-Greene-Kruskal electron Holes Near Magnetic Null Points

The files contain data generated from the theoretical model of electron holes in unmagnetized plasmas.

dryad36/100

Data from: A new null model approach to quantify performance and significance for ecological niche models of species distributions

Aim: Ecological niche modelling requires robust estimation of model performance and significance, but common evaluation approaches often yield biased estimates. Null models provide a solution but are rarely used in this field. We implemented an important modification to existing null-model tests, evaluating null models with the same withheld records that were used to evaluate the real model. We built and evaluated models across a range of modelling scenarios and for various performance measures using the algorithm Maxent and the monk parakeet (Myiopsitta monachus). Location: Native range in Southern America and global invasions predominantly in North/Central America and Europe Methods: We tested the ability of models built under 15 scenarios (five sets of calibration records and three settings that varied the level of model complexity) to predict spatially independent evaluation data in the invaded range (in effect, testing the models under spatial transfer). We quantified performance with measures of discriminatory ability and overfitting based on AUC and the omission error rate. We estimated null distributions of these measures and calculated effect size and significance. We determined how these estimates varied across modelling scenarios, comparing with two tests existing in the literature. Results: Performance varied starkly across modelling scenarios. As expected, the measures of overfitting agreed with each other and provided different information than that of discriminatory ability. However, high performance per se did not show strong association with high effect size and significance. Main Conclusions: Ecological niche models should be assessed with measures of effect size and significance based on appropriate null distributions, in contrast to several approaches existing in the literature. The proposed approach using independent evaluation data, implemented with our accompanying code, allows such estimates for either the same or a different region/time period, and it merits use and continued development.

opencc-zeroDec 2018View details →

opencc-by-4.0Dec 2013View details →

Zero-Postulation or Null-Postulation and Abstraction

The best bet is to find out the most fundamental components within the system and building a theory round these. In other words, a theory that is able to describe the world in totality has to keep the number of basic postulates it depends upon to zero or near zero. Zero Postulation gives rise to abstraction. The abstraction we are talking about here may be defined as, “Postulation of non-postulation” or, in other words, “A system of postulation that gives equal weights to all possible solutions inside the system and favors none of such solutions over others.”

opencc-by-4.0Jul 2017View details →

Electric potential and charge density for null emission

Fig6(c-d) plots electric potential and charge density for null emission. Parameters: $m=5, \tau_{\rm p}=4, v_0=0, \phi_g=1$.

opencc-by-4.0Nov 2023View details →

Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"

This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.

opencc-by-4.0Dec 2024View details →

Null device-independent prepare-and-prepare bipartite dimension test with a single joint measurement

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opencc-by-4.0Apr 2024View details →

SKS measurements and null measurements for seismic stations in Eritrea and Yemen

These files relate to "Channelized mantle flow through the Afar Triple Junction and around the Arabian plate: evidence from seismic anisotropy" by Gauntlett et al. (2024). Original seismic waveforms are from the Eritrea Seismic Project (<a href="https://doi.org/10.7914/sn/5h_2011">Hammond et al., 2011</a>) and the Young Conjugate Margins Lab in the Gulf of Aden network (<a href="10.7914/SN/XW_2009">Leroy et al., 2007</a>) and are publicly available through<a href="http://service.iris.edu/fdsnws/dataselect/1/">EarthScope Data Services</a>. The repository consists of three files: <ol> <li>SKS_ALL.csv</li> <li>SKS_NULL_ALL.csv</li> <li>stations_all.csv</li> </ol> The first file provides individual shear-wave splitting results reported in the study for the SKS phase. The columns are as follows:  <ul> <li>Station longitude</li> <li>Station latitude</li> <li>Orientation of the fast split shear wave (φ)</li> <li>Time delay between the fast and slow shear waves (dt)</li> <li>Error in phi</li> <li>Error in dt</li> <li>Event longitude</li> <li>Event latitude</li> <li>Event backazimuth</li> <li>Distance in degrees from event-station</li> <li>Event code</li> <li>Station name</li> </ul> The second file provides information on null results reported in the study, where no shear-wave splitting is observed for the SKS phase. The columns are as follows:  <ul> <li>Station longitude</li> <li>Station latitude</li> <li>Event longitude</li> <li>Event latitude</li> <li>Event backazimuth</li> <li>Distance in degrees from event-station</li> <li>Event code</li> <li>Station name</li> </ul> The station file contains the station name, the station latitude, station longitude and station elevation in km above sea level.

dryad36/100

Assessment of the stream invertebrate β-diversity along an elevation gradient using a bidimensional null model analysis

Aim: β-Diversity, commonly defined as the compositional variation among localities that links local diversity (α‐diversity) and regional diversity (γ‐diversity), can arise from two different ecological phenomena, namely the spatial species turnover (i.e. species replacement) and the nestedness of assemblages (i.e. species loss). However, any assessment that does not account for stochasticity in community assembly could be biased and misinform conservation management. In this study, we aimed to provide a better understanding of the overall ecological phenomena underlying stream β-diversity along elevation gradients, and at contributing to the rich debate on null model approaches to identify non-random patterns in the distribution of taxa. Location: Swiss Alpine region. Methods: Based on presence-absence data of 78 stream invertebrate families from 309 sites, we analyzed the effect size of non-random spatial distribution of stream invertebrates on the β-diversity and its two components (i.e. turnover and nestedness). We used a modelling framework that allows exploring the complete range of existing algorithm sused in null model analysis, and to assess how distribution patterns vary according to an array of possible ecological assumptions. Results: Overall, the turnover of stream invertebrates and the nestedness of assemblages were significantly lower and higher, respectively, than the ones expected by chance. This pattern increased with elevation, and the consistent trend observed along the altitudinal gradient, even in the most conservative analysis, strengthened our findings. Main conclusions: Our study suggests that deterministic distribution of stream invertebrates in the Swiss Alpine region is significantly driven by differential dispersal capacity and environmental stress gradients. As long as the ecological assumptions for constructing the null models and their implications are acknowledged, we believe that they still represent useful tools to measure the effect size of non-random spatial distribution of taxa on β-diversity.

opencc-zeroJul 2022View details →

opencc-by-4.0May 2024View details →

Null effect of perceived drum pattern complexity on the experience of groove (data set)

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