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920 results for “Functional analysis”
Meta-analysis on pulse disturbances reveals differences in functional and compositional recovery across ecosystems
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Functional profiling and evolutionary analysis of a marine microalgal virus pangenome
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An analysis of the effects of prior drying and vertebrate colonization on ecological functioning and vertebrate fitness
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Cover crop mixtures enhance multiple ecosystem functions: A global meta-analysis
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Data from: Association and function analysis of genetic variants and the risk of gestational diabetes mellitus in a southern Chinese population
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Why the long teeth? Morphometric analysis suggests different selective pressures on functional occlusal traits in Plio-Pleistocene African suids
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Data from: Integrative genomic analysis in African American children with asthma finds 3 novel loci associated with lung function
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Database for mi-faser: Functional sequencing read annotation for high precision microbiome analysis
<p><strong>[Database for mi-faser]</strong></p> <p><strong>mi-faser: </strong><em>microbiome - functional annotation of sequencing reads</em></p> <p>A super-fast ( < 20min/10GB of reads ) and accurate ( > 90% precision ) method for annotation of molecular functionality encoded in sequencing read data without the need for assembly or gene finding.</p> <p>Web Service: http://services.bromberglab.org/mifaser/|<br> Repository: https://bitbucket.org/bromberglab/mifaser_base/</p>
Analysis of fungal genomes reveals commonalities of intron gain or loss and functions in intron-poor species
<p>Datasets for DOI: <a href="https://doi.org/10.1101/2020.08.11.247098">10.1101/2020.08.11.247098</a>. Jupyter notebooks to reproduce the analysis of this study is available at <a href="https://github.com/Brookesloci/fungi_intron_paper_2020">GitHub</a></p> <ul> <li>annotations.total.txt.gz - gene annotation file of 350 fungi species and outgroups <em>Homo sapiens</em> and <em>Fonticula alba</em></li> <li>site_histories_41490_51_2L_1G.txt.gz - intron site histories of 263 fungi species and outgroups <em>Homo sapiens</em> and <em>Fonticula alba.</em></li> </ul>
Data from: Functional diversity of phyllostomid bats in an urban-rural landscape: a scale-dependent analysis
<p>Urbanization is one of the most pervasive landscape transformational processes responsible for novel selection agents promoting functional community homogenization. Bats may persist in those human environments, but the mechanisms responsible for their adaptability and the spatial scales in which landscape imposes environmental filtering remain poorly studied in the Neotropics. We tested the hypothesis that landscape composition interacts with the spatial scale to affect the functional diversity of phyllostomids in an urban-rural gradient. Based on functional traits, we calculated indices of functional richness, divergence, evenness, community-weighted means of morphological traits, and classified species into functional groups. We evaluated the changes of those variables in response to forest, grassland, and urbanized areas at 0.5, 1.25 and, 2 km scales. The number of functional groups, functional richness, and functional evenness tended to be higher in areas far from cities and with higher forest cover, whereas functional divergence increased in more urbanized areas. Our results show that the mean value of wing loading in the assemblage was negatively associated to landscape transformation at several spatial scales. However, environmental filtering driven by grass cover was particularly robust at the 500 m scale, affecting big-sized species with long pointed wings. Retaining natural forest in cattle ranging systems at ~12 km<sup>2</sup> appears to favor bat abundance evenness among functional types in the urban-rural landscape. Recognizing the scale of the effect on phyllostomid functional responses appears to be a fundamental issue for elucidating the spatial extent to which phyllostomid conservation planning in urban-rural landscapes should be addressed.</p>
Data from: Trait-based functional dietary analysis provides a better insight into the foraging ecology of bats
1. The degree of trophic specialization determines the ability of predators to cope with changing foraging conditions, but in predators that prey on hundreds of species it is challenging to assess, especially when prey identity varies among predator individuals and across space and time. 2. Here, we test the hypothesis that a bat species foraging on flying insects like moths will show ample flexibility in trophic niche, and this irrespective of phylogenetic relationships among moths, so as to cope with a high diversity of prey types that vary across seasons. We predict that individual bats will show functional dietary differences consistent with energetic requirements and hunting skills. 3. We used DNA metabarcoding to determine the diet of 126 Mediterranean horseshoe bats (Rhinolophus euryale) from two different sites during three seasons. Simultaneously, we measured moth availability and characterized the traits of 290 moth taxa. Next, we explored the relationship between phylogeny and traits of all consumed and available moth taxa. Finally, we assessed the relationship between individual traits of bats and traits related to prey profitability, for which we used the RLQ and fourth-corner statistical techniques. 4. Seasonality was the main factor explaining the functional dietary variation in adult bats, with moths consumed irrespective of their phylogenetic relationships. While adults consumed moths with a broad range in wing loading, body mass and echolocation detection ability, juveniles consumed slower, smaller and lighter moths, which suggests that young individuals may undergo some fitness gain and/or psychomotor learning process during which they would acquire more effective foraging skills. 5. Our approach revealed a degree of functional flexibility in the trophic niche previously unknown for an insectivorous bat. R. euryale consumed a wide variety of moth taxa differing in profitability throughout seasons and between ontogenetic stages. We showed the validity of trait-based approaches to gain new insights in the trophic specialization of predators consuming hundreds of species of prey.
Data from: Testing the validity of functional response models using molecular gut content analysis for prey choice in soil predators
Analysis of predator - prey interactions is a core concept of animal ecology, explaining structure and dynamics of animal food webs. Measuring the functional response, i.e. the intake rate of a consumer as a function of prey density, is a powerful method to predict the strength of trophic links and assess motives of prey choice, particularly in arthropod communities. However, due to their reductionist set-up, functional responses, which are based on laboratory feeding experiments, may not display field conditions, possibly leading to skewed results. Here, we tested the validity of functional responses of centipede predators and their prey by comparing them with empirical gut content data from field-collected predators. Our predator - prey system included lithobiid and geophilomorph centipedes, abundant and widespread predators of forest soils and their soil-dwelling prey. First, we calculated the body size-dependent functional responses of centipedes using a published functional response model in which we included natural prey abundances and animal body masses. This allowed us to calculate relative proportions of specific prey taxa in the centipede diet. In a second step, we screened field-collected centipedes for DNA of eight abundant soil-living prey taxa and estimated their body size-dependent proportion of feeding events. We subsequently compared empirical data for each of the eight prey taxa, on proportional feeding events with functional response-derived data on prey proportions expected in the gut, showing that both approaches significantly correlate in five out of eight predator - prey links for lithobiid centipedes but only in one case for geophilomorph centipedes. Our findings suggest that purely allometric functional response models, which are based on predator-prey body size ratios are too simple to explain predator - prey interactions in a complex system such as soil. We therefore stress that specific prey traits, such as defence mechanisms, must be considered for accurate predictions.
FIGURE 2. Discriminant-function analysis summarizing principal components 1 – 6 in Two new species of lizards from the Liolaemus lineomaculatus section (Squamata: Iguania: Liolaemidae) from southern Patagonia
FIGURE 2. Discriminant-function analysis summarizing principal components 1 – 6, for all six species of the L. lineomaculatus group. Black circles: L. morandae sp. nov.; white circles: L. avilae sp. nov.; black squares: L. lineomaculatus; white squares: L. kolengh; gray triangles: L. hatcheri; black triangles: L. silvanae.
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>
FIGURE 4. Discriminant function analysis depicting morphological differentiation within the C. vittatus-hansenae group. Close grey circle indicates C. vittatus Group II. Open circle denotes C. hansenae Group I. Closed black circle represents C. hansenae Group II in Re-evaluating the taxonomic status of Chiromantis in Thailand using multiple lines of evidence (Amphibia: Anura: Rhacophoridae)
FIGURE 4. Discriminant function analysis depicting morphological differentiation within the C. vittatus-hansenae group. Close grey circle indicates C. vittatus Group II. Open circle denotes C. hansenae Group I. Closed black circle represents C. hansenae Group II.
Fig. 23. Principal Components Analysis plot for 23 and 25 in Two new species of Vaejovis (Scorpiones: Vaejovidae) belonging to the mexicanus group from Aguascalientes, Mexico, with comments on the homology and function of the hemispermatophore
Fig. 23. Principal Components Analysis plot for 23 and 25 measurements taken from males and females, respectively. First and second PCA for males were body size and metasoma length (97% of variance explained), and for females it was patella and carapace lengths (92% of variance explained) [upper plate]. The variable vector dimensions that separate the principal components of males and females are shown in the lower plate.
TEM analysis of WS2 flakes functionalized with Au NP
<p>This dataset contains TEM investigation of WS2 flakes decorated with Au NP.</p>
Global analysis of cell behavior and protein localization dynamics reveals region-specific functions for Shroom3 and N-cadherin during neural tube closure
<p>Failures of neural tube closure are common and serious birth defects, yet we have a poor understanding of the interaction of genetics and cell biology during neural tube closure. Additionally, mutations that cause neural tube defects (NTDs) tend to affect anterior or posterior regions of the neural tube but rarely both, indicating a regional specificity to NTD genetics. To better understand the regional specificity of cell behaviors during neural tube closure, we analyzed the dynamic localization of actin and N-cadherin via high-resolution tissue-level time-lapse microscopy during Xenopus neural tube closure. To investigate the regionality of gene function, we generated mosaic mutations in shroom3, a key regulator or neural tube closure This approach elucidates new differences between cell behaviors during cranial/anterior and spinal/posterior neural tube closure, provides mechanistic insight into the function of shroom3 and demonstrates the ability of tissue-level imaging and analysis to generate cell-biological mechanistic insights into neural tube closure.</p>
Feeding in the Devonian antiarch placoderm fishes: a study based upon morpho-functional analysis of jaws
<p><span>Antiarch placoderm fishes were an abundant component of the Middle Paleozoic vertebrate assemblages. Despite a large number of known taxa and specimens, the morphology and function of the skeletal elements of their jaws is inadequately known. Because of this, questions regarding their feeding modes and their roles in the trophic webs remains open. We present a skeleto-muscular model of the antiarch jaw apparatus with an attempt to reconstruct its potential biomechanical function. The position of the upper jaw suborbital bones within the plane of the ventral side of the fish armor is suggested to represent the natural 'mouth closed' position. During mouth opening the suborbitals rotated rostrally with simultaneous depression and inward rotation of the infragnathals. The ball-and-socket jaw articulation might ensure this combined movement. Recently described lower jaw elements of <em>Livnolepis zadonica</em> (Obrucheva, 1983) and <em>Bothriolepis</em> sp. from the Upper Devonian (Lower Famennian) of Central Russia demonstrating very deep and porous blades of the oral division of the infragnathals attracted attention as to the structure of these bones in other antiarchs. Observed porosity reflects intense vascularization to supply blood to a connective tissue underlying a supposed keratinous sheath, which protected and strengthened the jaws, as well as made possible scraping tough food objects, such as thallus algae, from the substrate. </span></p> <p><span>Having evolved during the Silurian in the Pan-Cathaysian zoogeographical province, antiarchs migrated to Gondwana during the Emsian and later to Euramerica during the Eifelian. Supposedly, antiarchs became the first macrophytophagous vertebrates occupying the trophic level of primary consumers during the late Silurian – early Devonian. This event diversified the only previously existing predator-prey interrelationships between filter-feeding agnathans and predatory gnathostomes. </span></p>
Is species richness mediated by functional and genetic divergence? A global analysis in birds
<p class="MsoNormal">Unravelling why species richness varies shows such dramatic spatial variation is an ongoing challenge. Common to many theories is that increasing species richness requires a compensatory trade-off on an axis of species' ecology. Spatial variation in species richness may also affect genetic diversity if large numbers of coexisting, related species result in smaller population sizes. Here, we test whether increasing species richness results in differential occupation of morphospace by the constituent species, or decreases species' genetic diversity. We test for two potential mechanisms of morphological accommodation: denser packing in ecomorphological space, and expansion of the space. We then test whether species differ in their nucleotide diversity depending on allopatry or sympatry with relatives, indicative of potential genetic consequences of coexistence that would reduce genetic diversity in sympatry. We ask these questions in a spatially explicit framework, using a global database of avian functional trait measurements in combination with >120,000 sequences downloaded from GenBank. We find that higher species richness within families is not systematically correlated with either packing in morphological space or overdispersion but, at the Class level, we find a general positive relationship between packing and species richness, but that packing is comparatively greater in tropical points relative to their species richness. We find limited evidence that geographical co-occurrence with closely related species or tropical distributions decreases nucleotide diversity of nuclear genes; however, this requires further analysis. Our results suggest that avian families can accumulate species regionally with minimal tradeoffs or cost, implying that external biotic factors do not limit species richness.</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.