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123 results for “phenotypic variability”
Appendix 6 in Phenotypic variability in the shield morphology of wild- vs. lab-reared eumalacostracan larvae
Appendix 6. Principal components of Stomatopoda from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.
Appendix 5 in Phenotypic variability in the shield morphology of wild- vs. lab-reared eumalacostracan larvae
Appendix 5. Principal components of Raninidae from principal component analysis on the lateral shield outline and percentage of total variation in the data set explained by each principal component.
Appendix 4 in Phenotypic variability in the shield morphology of wild- vs. lab-reared eumalacostracan larvae
Appendix 4. Principal components of Hippoidea from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.
Appendix 3 in Phenotypic variability in the shield morphology of wild- vs. lab-reared eumalacostracan larvae
Appendix 3. Principal components of Galatheidae from principal component analysis on the shield outline and percentage of total variation in the data set explained by each principal component. A: Dorsal data set. B: Lateral data set.
Taking advantage from phenotype variability in a local animal genetic resource: identification of genomic regions associated with the hairless phenotype in Casertana pigs
<p>Ped and Map files for 96 Casertana breed pigs genotyped with Illumina BeadChip 60K Porcine.<br> The first field of the ped file contains the id of the farm (1az-6az).<br> The hairless phenotype, in the ped phenotype field, is codified as 1, the hairy phenotype is codified as 2.</p>
Fig. 4 in Variability Of The Phenotypic Composition Of Cepaea Hortensis (Gastropoda, Helicidae) In Western Ukraine: In Space And Time
Fig. 4. Theoretical ratio of genotype frequencies among carriers of the recessive allele (presence of bands) depending on the percentage of banded individuals at the site.
Fig. 2 in Variability Of The Phenotypic Composition Of Cepaea Hortensis (Gastropoda, Helicidae) In Western Ukraine: In Space And Time
Fig. 2. The number of studied sites in Lviv with different frequencies of banded, yellow and white unbanded shells. The numerals on the abscissa indicate the frequency intervals: 0 — absent, 1 — up to 10 %, 2 — from 11 to 20 %, etc.
Fig. 1 in Variability Of The Phenotypic Composition Of Cepaea Hortensis (Gastropoda, Helicidae) In Western Ukraine: In Space And Time
Fig. 1. Location of the sites in Lviv used for the quantitative study of the shell coloration variability of C. hortensis in 1999–2004 (A) and in 2015–2021 (B).
Figure 7 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 7. Median-joining network of 68 haplotypes from 485 wild boars from different regions of the world. Circle sizes are proportional to haplotype frequencies and numbers refer to haplotype codes in Table 2. Numbers on the branches indicate the number of nucleotide substitutions, if more than one. Major haplogroups are delimited by dashed lines. A, Asian; NE, Near Eastern; E1, European E1; E2, European E2. Haplotypes exclusive to Turkey are labeled in green.
Figure 3 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 3. Maximum likelihood phylogenetic tree of haplotypes of the Turkish wild boars (Sus scrofa) obtained in the present study, based on the partial D-loop sequences of mtDNA. Numbers above or below branches indicate bootstrap values. TR numbers refer to current haplotype numbers in Table 1 and H numbers refer to the published haplotype labels downloaded from GenBank (Table 2). E1: European 1 haplogroup/clade in Figure 5, NE: Near East haplogroup/ clade in Figure 5.
Figure 2 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 2. Various phenotypes of obtained Turkish wild boar individuals from different localities studied presently.
Figure 6 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 6. Bayesian inference tree based on 68 partial D-loop haplotypes of 485 wild boars (both obtained in this study and downloaded from GenBank). Posterior probabilities are indicated at nodes. Haplogroups: A, Asian; NE, Near Eastern; E1, European E1; E2, European E2. Outgroup taxa are Sus barbatus and Phacochoerus aethiopicus.
Figure 1 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 1. Location map of the 70 samples examined in this study: black dots indicate local samples of one or more individuals and numbers refer to locality names in Table 1. TT (Turkish Thrace, 1–4), SWA (Southwestern Anatolia, 5–11), CA (Central Anatolia, 12–26), NEA (Northeastern Anatolia, 27–30), and SEA (Southeastern Anatolia, 31–34).
Figure 5 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 5. Maximum likelihood tree based on 68 haplotypes from 485 wild boar D-loop region sequences (both obtained in this study and downloaded from GenBank). Numbers above or below branches indicate bootstrap support values. Haplogroups: A, Asian; NE, Near Eastern; E1, European E1; E2, European E2. Outgroup taxa are Sus barbatus and Phacochoerus aethiopicus.
Figure 4 in Mitochondrial DNA control region variability of wild boar Sus scrofa with various external phenotypes in Turkey
Figure 4. Bayesian phylogenetic tree constructed from haplotypes of the wild boar (Sus scrofa) samples collected in this study, based on the partial D-loop sequences of mtDNA. Posterior probabilities are indicated at nodes. TR numbers refer to current haplotype numbers in Table 1 and H numbers refer to the published haplotype labels downloaded from GenBank (Table 2). E1: European 1 haplogroup/clade in Figure 5, NE: Near East haplogroup/clade in Figure 5.
The effect of urbanisation and local environmental heterogeneity on phenotypic variability of a tropical treefrog
Open the record for dataset details and reuse information.
Temporal instability of lake charr phenotypes: Synchronicity of growth rates and morphology linked to environmental variables?
<p>Pathways through which phenotypic variation among individuals arise can be complex. One assumption often made in relation to intraspecific diversity is that the stability or predictability of the environment will interact with expression of the underlying phenotypic variation. <span>To address</span> biological complexity below the species level<span>, we investigated variability across years in morphology and annual growth increments between and within two sympatric lake charr <i>Salvelinus namaycush</i> ecotypes in Rush Lake, USA. </span>A rapid phenotypic shift in body and head shape was found within a decade. The magnitude and direction of the observed phenotypic change was consistent in both ecotypes, which suggests similar pathways caused the variation over time. Over the same time period, annual growth increments declined for both lake charr ecotypes and corresponded with a consistent phenotypic shift of each ecotype. Despite ecotype-specific annual growth changes in response to winter conditions, the observed annual growth shift for both ecotypes was linked, to some degree, with variation in the environment. Particularly, a declining trend in regional cloud cover was associated with an increase of early stage (ages 1-3) annual growth for lake charr of Rush Lake. Underlying mechanisms causing changes in growth rates and constrained morphological modulation are not fully understood. An improved knowledge of the biology hidden within the expression of phenotypic variation promises to clarify our understanding of temporal morphological diversity and instability.</p>
Supplementary data for Inter-provenance variability and phenotypic plasticity of wood and leaf traits related to hydraulic safety and efficiency in seven European beech (Fagus sylvatica L.) provenances differing in yield
<p>Dataset and supplementary file for <strong>Inter-provenance variability and phenotypic plasticity of wood and leaf traits related to hydraulic safety and efficiency in seven European beech (<em>Fagus sylvatica</em> L.) provenances differing in yield </strong>paper.</p> <p>The ANFS_data file includes individual level measurements of xylem safety and efficiency traits, leaf traits and growth among 7 provenances planted at two common garden sites in Germany and Slovakia. More details related to the methodology might be found in the published paper by Kurjak et al. 2024.</p> <p>The ANFS_supplementary file includes the test for differences in distance to tip between sites and provenances, based on the branch diameter-branch length scaling.</p>
Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments
<p>These are the data and code to go with "<strong>Rapid evolution of thermal tolerance and phenotypic plasticity in variable environments</strong>"</p> <p><strong>Figure 01 takes the following data/scripts:</strong></p> <p><a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20210610_Thally02_Figure01_plot_and_stats.R">20210610_Thally02_Figure01_plot_and_stats.R </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/track_keeper.csv">track_keeper.csv </a> and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/corr_Response%20growth%20.csv">corr_Response growth .csv </a>. These files contain growth rates per transfers for all selection environments throughout the experiment and growth rates in correlated environments during reciprocal transplants, respectively. </p> <p><strong>Figure 02 takes the following data/scripts:</strong></p> <p> </p> <p><a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20210610_Thally02_Figure02_plot_and_stats.R">20210610_Thally02_Figure02_plot_and_stats.R </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20161120_res_logis_t000.csv">20161120_res_logis_t000.csv </a>and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20161123_resloglint300.csv">20161123_resloglint300.csv .</a> These files contain the output of the shapes of the growth curves (i.e. information on lag time , growth at µmax, K etc) for all samples in all selection environments at t0 and t300, respectively</p> <p><strong>The remaining figures - position not clear at time of submission - take the following data/script. </strong></p> <p>For plasticity in FRRF data, the script 20181204_FRRF_plasticity.R takes the FRRF raw data contained in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/allfvfmdata_thally_t300_t000.csv">allfvfmdata_thally_t300_t000.csv </a>. Extracted parameters are in files <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/CvaluesThally02.csv">CvaluesThally02.csv</a>, <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/psiPSI_slope_intercept.csv">psiPSI_slope_intercept.csv</a>, and <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/rP_extracted_values.csv">rP_extracted_values.csv </a> and can be analysed using the R script <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/extracted%20parameter%20plots.R">extracted parameter plots.R .</a> R script <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/FRRF%20visualisation%20only%20.R">FRRF visualisation only .R </a> is for visualisation only, as the title suggests. </p> <p>For comparing plasticity/growth , the data are in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/20170327_giantbigtable.csv">20170327_giantbigtable.csv </a>, and can be visualised/analysed in <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/plast%20vs%20growth.R">plast vs growth.R </a></p> <p>In order to recreate the AMOVAS based on SNVs, use <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/all_variants_fixed-only_using_5x_depth_threshold.csv?versionId=01cbf6bf-fe16-4e7d-aae8-58c527596ebb">all_variants_fixed-only_using_5x_depth_threshold.cvs </a> with <a href="https://zenodo.org/api/files/cc14086e-84ed-4a13-b2d4-cf3cda0d669f/amova%20thally02.R?versionId=e9c21378-ee37-4418-9ce1-76db821e645a">amova thally02.R </a></p> <p>For additional information, please contact elisa.schaum@uni-hamburg.de </p>
Fig. 5 in Understanding the extent of phenotypic variability in accessions of Paspalum urvillei Steud. from the USDA NPGS
Fig. 5. UPGMA of Paspalum urvillei accessions from USDA germplasm.
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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.