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Text-fig. 56. Number of specimens and number of species for the four major plant groups recovered in the Torres Vedras mesofossil flora. Unidentified specimens such as seed fragments, stamen fragments without pollen grains, coprolites without recognizable plant fragments and strongly distorted specimens are not included in this overview. in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 56. Number of specimens and number of species for the four major plant groups recovered in the Torres Vedras mesofossil flora. Unidentified specimens such as seed fragments, stamen fragments without pollen grains, coprolites without recognizable plant fragments and strongly distorted specimens are not included in this overview.
Text-fig. 51. Number of specimens and number of species for the five categories of angiosperms distinguished from the Catefica mesofossil flora. in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 51. Number of specimens and number of species for the five categories of angiosperms distinguished from the Catefica mesofossil flora.
FIG. 7 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 7. — Distribution of Cetrelia cetrarioides (Delise) W.L. Culb.& C.F.Culb. in Hungary after revision.
FIG. 11 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 11. — Distribution of Cetrelia olivetorum (Nyl.) W.L. Culb. & C.F.Culb.in Hungary after revision.
FIG. 4 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 4. — Microcrystal tests of A, imbricaric; B, perlatolic; C, olivetoric acids and D, atranorin in GE solvent (glycerine – acetic acid, 3:1 v/v). Scale bars: 50 µm.
FIG. 6 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 6. — Distribution of Cetrelia cetrarioides (Delise) W.L. Culb. & C.F. Culb.in Hungary before revision.
FIG. 10 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 10. — Distribution Cetrelia olivetorum (Nyl.) W.L. Culb.& C.F. Culb.in Hungary before revision.
FIG. 2. — A in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 2. — A detail of the chromatographic plate HPTLC nr 74/2014 developed in solvent system C presenting all species under UV 254 nm (A), and sprayed with water (B). Specimens in positions A10-A17 are A10: C. olivetorum (Nyl.) W.L. Culb. & C.F. Culb. (BP 21529), A11: C. monachorum (Zahlbr.) W.L. Culb. & C.F. Culb. (BP 85318), A12: C. olivetorum (BP 84893), A13: C. cetrarioides (Delise) W.L. Culb. & C.F. Culb. (BP 21538), A14: C. olivetorum (BP 22787), A15: C. monachorum (BP 45013), A16: C. olivetorum (BP 21508), A17: C. chicitae (W.L. Culb.) W.L. Culb. & C.F. Culb. (BP 93416). Abbreviations of LSMs are according to Table 2.
FIG. 5 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 5. — Thalline lobes with marginal soralia of A, Cetrelia cetrarioides (Delise) W.L. Culb. & C.F. Culb.; B, C. chicitae (W.L. Culb.) W.L. Culb. & C.F. Culb.; C, C. monachorum (Zahlbr.) W.L. Culb. & C.F. Culb.; and D, C. olivetorum (Nyl.) W.L. Culb. & C.F. Culb. Scale bars: 1 mm.
FIG. 1 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 1. — Differential morphological and chemical features in Cetrelia W.L. Culb. & C.F. Culb. species: A, large, not raised pseudocyphellae on upper cortex of C. chicitae (W.L. Culb.) W.L. Culb. & C.F. Culb.; B, small, raised pseudocyphellae on upper cortex of C. monachorum (Zahlbr.) W.L. Culb. & C.F. Culb.; C, brown lower cortex of C. cetrarioides (Delise) W.L. Culb. & C.F. Culb. without rhizines; D, C+ reaction by NaOCl on marginal soralia of Cetrelia olivetorum (Nyl.) W.L. Culb. & C.F. Culb. Scale bars: 500 µm.
FIG. 13 in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 13. — The number of Cetrelia W.L. Culb. & C.F. Culb. species after revision in various parts of Hungary.
FIG. 3. — A in Analysis of lichen secondary chemistry doubled the number of Cetrelia W.L. Culb. & C.F. Culb. species (Parmeliaceae, lichenised Ascomycota) in Hungary
FIG. 3. — A detail of the chromatographic plate TLC nr 1903 developed in solvent system C presenting C. chicitae (W.L. Culb.) W.L. Culb. & C.F. Culb. specimens under UV 254 nm (A), UV 366 nm (B) and sprayed with anisaldehyde/sulphuric acid (C). Specimens in positions 3-9 are: 3, from Page County, Virginia, United States (BP 75822); 4, from the Bükk Mts, Hungary (BP 71276); 5, from the Zemplén Mts, Hungary (BP 49905); 6, from Pocahontas County, West Virginia, United States (BP 91365); 7, from Ukraine (BP 44999); 8, from Romania (BP 85320); 9, from Poland (BP 21508). Abbreviations of LSMs are according to Table 2.
Data used in: Heritability and variance components of seed size in wild species: influences of breeding design and the number of genotypes tested
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SGS-LTER Long Term Nitrogen Percentages in Grass, Forb and Shrub Species on the Central Plains Experimental Range, Nunn, Colorado, USA 1983 - 2008, ARS Stusy Number 6
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83462. Aboveground plant nitrogen dynamics monitoring consists of two separate data sets. a) Long-term peak-crop nitrogen concentrations have been sampled since 1983 annually from sites sampled for ANPP estimates across the CPER. Plots are clipped for ANPP in August each year and include moderately grazed sites in sections 24 and 25, ungrazed treatments at ESA and owl creek, coarse textured soils in owl creek, fine textured soils in section 25, as well as three catena topopositions in section 24. These datasets have been designed for monitoring and so it is advised to consider calcuating average based at the transect level. B) Seasonal dynamics of life-form (dominant grass, forb, shrub species) nitrogen concentrations were obtained from random grab samples of aboveground plant tissue are taken monthly from May-Aug. and in Oct., Dec., Feb., and April from 1983 – 2007 at sites where ANPP has been collected since 1983 (ESA, ridge, mid-slope and swale in section 24). The objectives are to assess annual/seasonal weather and site productivity/management with quantity and quality of forage and/or litter production. Combined, these two data sets also provide an estimate of nitrogen yield
Fertilization Above and Below Ground Biomass and Species Number on Hog Island Dunes, 1991
A one-year study on the accreting north end of Hog Island, VA, provided the opportunity to quantify amounts of plant biomass along a natural dune chronosequence (24, 36, and 120+ year-old dunes) and biomass response to experimental additions of nitrogen. Total aboveground biomass, root biomass, and species number in 1-m2 plots on the dunes across the North Hog Chronosequence. Treatment plots included screened, fertilized, and screened & fertilized.
Copy number variants outperform SNPs to reveal genotype-temperature association in a marine species
<p>Copy number variants (CNVs) are a major component of genotypic and phenotypic variation in genomes. To date, our knowledge of genotypic variation and evolution has largely been acquired by means of single nucleotide polymorphism (SNPs) analyses. Until recently, the adaptive role of structural variants (SVs) and particularly that of CNVs has been overlooked in wild populations, partly due to their challenging identification. Here, we document the usefulness of Rapture, a derived reduced‐representation shotgun sequencing approach, to detect and investigate copy number variants (CNVs) alongside SNPs in American lobster (<i>Homarus americanus</i>) populations. We conducted a comparative study to examine the potential role of SNPs and CNVs in local adaptation by sequencing 1,141 lobsters from 21 sampling sites within the southern Gulf of St. Lawrence, which experiences the highest yearly thermal variance of the Canadian marine coastal waters. Our results demonstrated that CNVs account for higher genetic differentiation than SNP markers. Contrary to SNPs, for which no significant genetic–environment association was found, 48 CNV candidates were significantly associated with the annual variance of sea surface temperature, leading to the genetic clustering of sampling locations despite their geographic separation. Altogether, we provide a strong empirical case that CNVs putatively contribute to local adaptation in marine species and unveil stronger spatial signal of population structure than SNPs. Our study provides the means to study CNVs in nonmodel species and highlights the importance of considering structural variants alongside SNPs to enhance our understanding of ecological and evolutionary processes shaping adaptive population structure.</p>
Data from: Weighting effective number of species measures by abundance weakens detection of diversity responses
1. The effective number of species (ENS) has been proposed as a robust measure of species diversity that overcomes several shortcomings of both diversity indices and species richness measures. However, it is not yet clear if ENS improves interpretation and comparison of biodiversity monitoring data, and ultimately resource management decisions. 2. We used simulations of five stream macroinvertebrate assemblages and spatially extensive field data of stream fishes and mussels to show (1) how different ENS formulations respond to stress and (2) how diversity-environment relationships change with values of q, which weight ENS measures by species abundances. 3. Values of ENS derived from whole simulated assemblages with all species weighted equally (true species richness) steadily decreased as stress increased, and ENS-stress relationships became weaker and more different among assemblages with increased weighting. 4. The amount of variation in ENS across the fish and mussel assemblages that was associated with environmental gradients decreased with increasing q. 5. Synthesis and applications: ENS does not improve interpretability of how diversity responds to stress or natural environmental gradients, and incorporating relative abundance into species diversity measures as implemented in ENS can actually weaken detection of diversity responses. Ecologists need to be cautious about use and interpretation of diversity measures whose values are jointly influenced by richness and evenness, including ENS, and instead separately assess species richness, species evenness, and compositional change in ecological communities.
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 4. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI and 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 3. - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
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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.