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23 results for “Compositional similarity”
Supplementary file 1 from: Moliner Cachazo L, Makati K, Chadwick MA, Catford JA, Price BW, Mackay AW, Guiry MD, Murray-Hudson M, Murray-Hudson F (2023) A review of the freshwater diversity in the Okavango Delta and Lake Ngami (Botswana): taxonomic composition, ecology, comparison with similar systems and conservation status. Aquatic Sciences
<p>Dataset with 2,204 freshwater species from the Okavango Delta and Lake Ngami (Botswana), with additional 355 species found in other areas of Botswana that are likely to be present in the study region. The dataset covers the following groups: amphibians, birds, fishes, macroinvertebrates, macrophytes, mammals, reptiles, phytoplankton, and zooplankton. The following information is given for each species: status in the Okavango Delta and Lake Ngami (present/potentially present); conservation status globally, Phylum, Class, Order, Family, Genus, species name, cited synonyms, common name, habitat, presence in high water, presence in low water, ecology, distribution in continental Africa, confirmed locations in the Okavango Delta, site coordinates, references, notes.</p>
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Рис. 1. ÀенΔрограмма схоΔства (коэффициент Жаккара) состава насеΛения герпетобионтных жесткокрыΛых поймы разных Λет (2008–2011 гг.) Fig. 1. The dendrogram of faunistic similarity (Jacquard coefficient) for the population composition of herpetobiont beetles in different years (2008–2011) in Population Dynamics For Herpetobiont Beetles (Coleoptera) In The Floodplain Of A Small Tributary In The Lower Reaches Of The Irtysh
Рис. 1. ÀенΔрограмма схоΔства (коэффициент Жаккара) состава насеΛения герпетобионтных жесткокрыΛых поймы разных Λет (2008–2011 гг.) Fig. 1. The dendrogram of faunistic similarity (Jacquard coefficient) for the population composition of herpetobiont beetles in different years (2008–2011)
Рис. 2. ΔенΑрограмма схоΑства фаун Αонных беспозвоночных воΑотоков заповеΑника «КомсомоΛьский» (UPGMA, коэффициент Сёренсена) Fig. 2. Dendrogram of the fauna similarity of benthic invertebrates of the Komsomolsky Nature Reserve watercourses (UPGMA, Sorensen coefficient) in Taxonomic composition of benthic invertebrates of the Komsomolsky Nature Reserve watercourses (Khabarovsky Region)
Рис. 2. ΔенΑрограмма схоΑства фаун Αонных беспозвоночных воΑотоков заповеΑника «КомсомоΛьский» (UPGMA, коэффициент Сёренсена) Fig. 2. Dendrogram of the fauna similarity of benthic invertebrates of the Komsomolsky Nature Reserve watercourses (UPGMA, Sorensen coefficient)
Рис. 2. UPGMA-ΑенΑрограмма схоΑства виΑового состава (А) и фаунистическая структура (B) сообществ земΛероек в пяти ΛокаΛитетах Амурской обΛасти: ЗЗ — Зейский заповеΑник; НЗ — Норский заповеΑник; ХЗ — Хинганский заповеΑник; ЧФЗ — ХинганоАрхаринский заказник; НБС — территория зоны вΛияния Нижнебурейской ГЭС. ΔТФ — Αревнетаежная фауна; БФ — бореаΛьная фауна; НФ — немораΛьная фауна; Αр. — преΑставитеΛи Αругих фауногенетических группировок (пояснения в тексте) Fig. 2. UPGMA dendrogram of species composition similarity (A) and fauna structure (B) of shrew communities in five Amur region localities: ZZ — Zeya nature reserve; NZ — Norsky nature reserve; KhZ — Khingansky nature reserve; ChFZ — KhinganoArkharinsky nature reserve; NBS — the area influenced by the Nizhnebureyskaya hydroelectric power station. DTP — ancient taiga fauna; BF — boreal fauna; NF — nemoral fauna; others — representatives of other faunagenetic groups (explained in the text) in Shrew species composition and fauna structure in the Norsky reserve
Рис. 2. UPGMA-ΑенΑрограмма схоΑства виΑового состава (А) и фаунистическая структура (B) сообществ земΛероек в пяти ΛокаΛитетах Амурской обΛасти: ЗЗ — Зейский заповеΑник; НЗ — Норский заповеΑник; ХЗ — Хинганский заповеΑник; ЧФЗ — ХинганоАрхаринский заказник; НБС — территория зоны вΛияния Нижнебурейской ГЭС. ΔТФ — Αревнетаежная фауна; БФ — бореаΛьная фауна; НФ — немораΛьная фауна; Αр. — преΑставитеΛи Αругих фауногенетических группировок (пояснения в тексте) Fig. 2. UPGMA dendrogram of species composition similarity (A) and fauna structure (B) of shrew communities in five Amur region localities: ZZ — Zeya nature reserve; NZ — Norsky nature reserve; KhZ — Khingansky nature reserve; ChFZ — KhinganoArkharinsky nature reserve; NBS — the area influenced by the Nizhnebureyskaya hydroelectric power station. DTP — ancient taiga fauna; BF — boreal fauna; NF — nemoral fauna; others — representatives of other faunagenetic groups (explained in the text)
NSRC-Search: Efficient searching for similar protein sequences of non-standard amino acid composition
<p>This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)</p>
Supplementary material 3 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Common OTU lists and Statistical analysis : Explanation note: This file contains detected OTUs in both methods and biodiversity analysis (GLM and t-test)
Supplementary material 2 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Biodiversity workflow in R : Explanation note: Bundle of files for biodiversity analysis in R. All necessary input files and a commented script of R-commands are provided.
Supplementary material 4 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Master data sheet : Explanation note: Spreadsheet file containing information about read abundances of operational taxonomic units (OTUs) and sample metadata. Here, data were prepared for subsequent biodiversity analysis in R.
Supplementary material 1 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Bioinformatics pipeline : Explanation note: This file provides all steps and commands necessary for quality filtering and demultiplexing of raw paired fastq sequences.
Data from: Bee communities along a prairie restoration chronosequence: similar abundance and diversity, distinct composition
Recognition of the importance of bee conservation has grown in response to declines of managed honey bees and some wild bee species. Habitat loss has been implicated as a leading cause of declines, suggesting that ecological restoration is likely to play an increasing role in bee conservation efforts. In the Midwestern USA, restoration of tallgrass prairie has traditionally targeted plant community objectives without explicit consideration for bees. However, restoration of prairie vegetation is likely to provide ancillary benefits to bees through increased foraging and nesting resources. We investigated community assembly of bees across a chronosequence of restored eastern tallgrass prairies and compared patterns to those in control and reference habitats (old fields and prairie remnants, respectively). We collected bees for three years and measured diversity and abundance of in-bloom flowering plants, vegetation structure, ground cover, and surrounding land use as predictors of bee abundance and bee taxonomic and functional diversity. We found that site-level variables, but not site type or restoration age, were significant predictors of bee abundance (bloom diversity: p = 0.004, bare ground cover: p = 0.02) and bee diversity (bloom diversity: p = 0.01). There were significant correlations between overall composition of bee and blooming plant communities (mantel test: p = 0.002), and both plant and bee assemblages in restorations were intermediate between those of old fields and remnant prairies. Restorations exhibited high bee beta diversity, i.e., restored sites' bee assemblages were taxonomically and functionally differentiated from each other. This pattern was strong in younger restorations (< 20 years old), but absent from older restorations (> 20 years), suggesting restored prairie bee communities become more similar to one another and more similar to remnant prairie bee communities over time with the arrival of more species and functional groups of bees. Our results indicate that old fields, restorations, and remnants provide habitat for diverse and abundant bee communities, but continued restoration of old fields will help support and conserve bee communities more similar to reference bee communities characteristic of remnant prairies.
Data from: Similar hybrid composition among different age and sex classes in the Myrtle–Audubon's warbler hybrid zone
Hybrid zones provide a key natural context within which to study the barriers between incipient species. In some avian hybrid zones, there is indirect evidence of selection against hybrid offspring, yet the source of that selection is often unclear. We examined the frequency distribution of hybrids between Myrtle Warblers (Setophaga coronata coronata) and Audubon's Warblers (S. c. auduboni), using data to quantify—for the first time at a genomic scale—the composition of hybrids in this hybrid zone. We sampled birds during the breeding season and during fall migration and compared the frequencies of hybrids of different sex and age classes. Specifically, we tested for evidence of early-generation hybrids being significantly under- or over-represented in any of these classes, as would be expected if hybrids have lower or higher fitness than non-hybrids. We found that the genomic composition of birds in the hybrid zone spans the full ancestry spectrum. Across all our sampling periods, we found an excess of birds that had more Audubon's ancestry, with a stronger bias toward Audubon's ancestry in fall migrants than in breeding birds, consistent with asymmetric introgression. Notably, we did not find any differences in hybrid frequencies between juvenile and adult age classes or between males and females. Therefore, our results do not support large differences in viability between male and female hybrids or between different age classes of hybrids.
Data from: Bee communities along a prairie restoration chronosequence: similar abundance and diversity, distinct composition
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Data from: Similar hybrid composition among different age and sex classes in the Myrtle–Audubon's warbler hybrid zone
Open the record for dataset details and reuse information.
Figure 5 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 5 - Relative abundance distribution of fungal leaf-inhabiting endophytes of beech among the five main trophic guilds as revealed by analysis with FUNGuild (Nguyen et al. 2016). A compares the two localities for each trophic guild on the basis of Illumina data B compares the two localities for each trophic guild on the basis of cultivation data.
Figure 4 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 4 - Relative abundance of fungal leaf-inhabiting endophytes of beech among the five main trophic guilds as revealed by analysis with FUNGuild (Nguyen et al. 2016). A compares the two methods for each trophic guild and unassigned data. B displays the trophic guilds and unassigned taxa for Illumina data, C for cultivation data. Abbreviations in [B and C]: U = Unassigned, P = Pathotrophs, PSa = Patho-Saprotrophs, PSy = Patho-Symbiotrophs, Sa = Saprotrophs, Sy = Symbiotrophs
Figure 2 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 2 - . Principal coordinate analysis (PCoA) of fungal leaf-inhabiting endophytes of beech display strongly differing assemblages obtained with Illumina sequencing and cultivation. Both methods revealed differing mycobiomes from valley and from mountain leaves, although these differences were less pronounced for cultivation data. Abbreviations: IM = Illumina data from mountain samples, IV = Illumina data from valley samples, CM = cultivation data from mountain samples, CV = cultivation data from valley samples
Figure 3 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 3 - Abundance distribution of the 20 most abundant orders of fungal leaf-inhabiting endophytes of beech on a logarithmic scale. Three of the five most abundant orders from high-throughput sequencing were also most abundant in cultivation data.
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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.