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1,363 results for “phenotypic data”

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dryad32/100

Data from: Intra-specific variation in responses to habitat restoration: Could artificial reefs increase spatiotemporal segregation between migratory phenotypes of lake sturgeon?

Habitat restoration is an important tool used to conserve biodiversity and restore species, but its effects are notoriously difficult to predict. Although outcomes of restoration projects are usually assessed using indices of species abundance and diversity, phenotypic differences among individuals within species are likely associated with differing responses to restored habitats. Here, we use lake sturgeon (Acipenser fulvescens) as a case study to illustrate how responses to habitat restoration can differ between phenotypes and potentially lead to unanticipated effects on populations. North America's St. Clair River supports one of the largest remaining populations of lake sturgeon but has lost much spawning habitat due to its role as a major industrial corridor between the Laurentian Great Lakes Erie and Huron. Two artificial reefs were recently built in the lower and middle segments of the river to increase the available sturgeon spawning habitat. Interestingly, lake sturgeon in the St. Clair River express different migratory phenotypes that may be associated with different likelihoods of colonizing artificial reefs. Acoustic telemetry revealed that artificial reefs were more likely to be used by sturgeon that migrated downstream to overwinter in Lake St. Clair than those that migrated upstream to overwinter in Lake Huron. Furthermore, increasing time spent at the artificial reefs by Lake St. Clair migrants was associated with later arrival to and shorter occupancy of the river's only natural spawning site, the primary location where the two phenotypes have opportunity to interbreed. Additional research is necessary to determine the ultimate impacts of the artificial reefs on lake sturgeon populations; nevertheless, our study showed phenotype-specific opportunity to colonize restored habitat and a mechanism through which this could lead to changes in gene flow. Our results illustrate the importance of considering intra-specific diversity when planning restoration projects and assessing the effects on populations.

opencc-zeroMar 2023View details →

dryad32/100

Data from: Spatiotemporal segregation by migratory phenotype indicates potential for assortative mating in lake sturgeon

Migratory diversity can promote population differentiation if sympatric phenotypes become temporally, spatially, or behaviorally segregated during breeding. In this study, the potential for spatiotemporal segregation was tested among three migratory phenotypes of lake sturgeon (Acipenser fulvescens) that spawn in the St. Clair River of North America's Laurentian Great Lakes but differ in how often they migrate into the river and in which direction they move after spawning. Acoustic telemetry over nine years monitored use of two major spawning sites by lake sturgeon that moved north to overwinter in Lake Huron or south to overwinter in Lake St. Clair. Lake St. Clair migrants were further distinguished by whether they migrated into the St. Clair River each year (annual migrants) or intermittently (intermittent migrants). Social network analyses indicated lake sturgeon generally co-occurred with individuals of the same migratory phenotype more often than with different migratory phenotypes. A direct test for differences in space use revealed one site was almost exclusively visited by Lake St. Clair migrants whereas the other site was visited by Lake Huron migrants, intermittent Lake St. Clair migrants, and, to a lesser extent, annual Lake St. Clair migrants. Analysis of arrival and departure dates indicated an opportunity for co-occurrence at the site visited by all phenotypes but showed Lake Huron migrants arrived approximately two weeks before Lake St. Clair migrants. Taken together, our results indicated partial spatiotemporal segregation of migratory phenotypes that may generate assortative mating and promote population differentiation.

opencc-zeroMar 2023View details →

zenodo32/100

Data for "Bootstrapping outperforms community-weighted approaches for estimating the shapes of phenotypic distributions"

This repository contains datasets used in the manuscript entitled "Bootstrapping outperforms community-weighted approaches for estimating the shapes of phenotypic distributions" by Maitner et al.  For details of these datasets, see https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution. All datasets contain individual (and in some cases, organ-level) trait measurements. The dataset "all_traits_unscaled_RMBL.rds" was compiled by  Christine Lamanna, Lindsey L Sloat, Andrew J. Kerkhoff, and Brian J. Enquist, Full details in https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution The dataset "Julies_panama_data.xlsx" was compiled by Julie Messier and collaborators, full details here: https://doi.org/10.1111/j.1461-0248.2010.01476.x The dataset "TreefrogTadpoles.xlsx" was compiled by Nick Rasmussen, full details here: https://www.jstor.org/stable/44082203  The dataset "zooplankton_2019.zip" was compiled by Ewa Merz and Francesco Pomati. For more details, see www.aquascope.ch , <a href="https://github.com/mbaityje/plankifier">https://github.com/mbaityje/plankifier</a>, <a href="https://github.com/tooploox/SPCConvert">https://github.com/tooploox/SPCConvert</a>, and https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution .

opencc-by-4.0Apr 2023View details →

dryad32/100

Data for: Phenotypic outcomes of predator-prey coevolution are predicted by landscape variation in climate and community composition

<ol> <li>Landscape patterns of phenotypic coevolution are determined by variation in the outcome of predator-prey interactions. These outcomes may depend not only on the functional phenotypes that mediate species interactions but also on aspects of the environment that enable encounters between coevolutionary partners.</li> <li>Exploring the relationship between coevolutionary traits and the environment requires extensive sampling across the range of the interaction to determine the relationship between local ecological variation and coevolution.</li> <li>In this study, we synthesized >30 years of data on predator-prey interactions between toxic newts (Taricha granulosa) and their snake predators (Thamnophis sirtalis) to explore the environmental predictors of arms race escalation.</li> <li>We found that geographic variation in phenotypes at the interface of coevolution was best predicted by a combination of community and climatic variation. Coevolutionary phenotypes were greatest in environments with climate favorable for newt-snake overlap. We found prey toxicity was elevated in regions with more predator species, and predator resistance was higher in regions with more prey species.</li> <li>Our results suggest specific environmental conditions reinforce the process of coevolution, signifying the phenotypic outcomes of coevolutionary arms races are sensitive to local ecological contexts that vary across the landscape. </li> </ol>

opencc-zeroMay 2023View details →

zenodo32/100

Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering (data)

MIBI-TOF data for lymph node dataset reported in Liu et al., Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering 1. mibi_single_channel_tifs.zip: Single-channel MIBI-TOF images Folders are labeled according to the field-of-view (FOV) number. Each folder contains single-channel TIFFs for each marker in the panel. Images are 1024x1024 pixels, 500 um. See paper for details. 2. segmentation.zip: Segmentation output of MIBI-TOF images Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021). Output of Mesmer that delineates the single cells in each of the images is included. 3. source_data.zip: Source data files for figures <ul> <li>pixel_ccs_allpreprocessing.csv: Cluster consistency score (CCS) for all pixels using all preprocessing steps, related to Fig. 2d-f, Supp. Fig. 4,5,9,10</li> <li>pixel_ccs_nopixelnorm.csv: CCS for all pixels where pixel normalization was left out, related to Fig. 2f, Supp. Fig. 6</li> <li>pixel_ccs_nochannelnorm.csv: CCS for all pixels where channel normalization was left out, related to Fig. 2f, Supp. Fig. 8</li> <li>pixel_ccs_passes1.csv: CCS for all pixels where 1 pass was used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_passes100.csv: CCS for all pixels where 100 passes were used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_sigma0.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma1.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma3.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma0_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma1_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma2_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 2 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma3_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_nodes15.csv: CCS for all pixels where 15 nodes were used for SOM training, related to Supp. Fig. 9e</li> <li>pixel_ccs_threshold80.csv: CCS for all pixels where a threshold of 80% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_ccs_threshold98.csv: CCS for all pixels where a threshold of 98% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_info_comparison_table.csv: Number of pixels that were assigned to a cluster outside of cell segmentation masks, related to Fig. 3d</li> <li>single_cell_pixel_composition_table.csv: Pixel composition information for each single cell, related to Fig. 5, Supp. Fig 16</li> <li>single_cell_integrated_expression_table.csv: Integrated expression per cell, output by Mesmer, related to Fig. 5, Supp. Fig. 16</li> <li>cell_silhouette_scores.csv: Silhouette scores for comparing integrated expression and pixel composition, related to Fig. 5d</li> <li>cell_silhouette_scores_undefinedremoved.csv: Silhouette scores for comparing integrated expression and pixel composition where undefined cells were removed, related to Fig. 16g</li> <li>cell_silhouette_scores_preprocessed.csv: Silhouette scores for comparing integrated expression and pixel composition where pixels were preprocessed before integrating expression, related to Fig. 17d</li> <li>cell_silhouette_scores_ilastik_cellprofiler.csv: Silhouette scores for comparing integrated expression and pixel composition where segmentation masks were obtained using Ilastik/CellProfiler, related to Fig. 18b</li> <li>cell_ccs_pixel_composition.csv: CCS for all cells using pixel composition for clustering, related to Supp. Fig. 16e, 17c</li> <li>cell_ccs_integrated_expression.csv: CCS for all cells using integrated expression for clustering, related to Supp. Fig 16e-f</li> <li>cell_ccs_integrated_expression_preprocessed.csv: CCS for all cells using integrated expression for clustering where data was preprocessed before integrating, related to Supp. Fig 17c</li> <li>cytof_ccs.csv: CCS of the CyTOF dataset used as a benchmark, related to Supp. Fig. 4c,d</li> <li>scrnaseq_ccs.csv: CCS of the scRNA-seq dataset used as a benchmark, related to Supp. Fig. 4c,e</li> <li>pixel_phenotype_maps: TIFFs where pixel value corresponds to pixel cluster number as reported in the paper</li> <li>cell_phenotype_maps: TIFFs where pixel value corresponds to cell cluster number as reported in the paper</li> <li>runtime_analysis_pixel.csv: Runtime analysis of pixel clustering in Pixie, related to Supp. Fig. 22a</li> <li>runtime_analysis_cell.csv: Runtime analysis of cell clustering in Pixie, related to Supp. Fig. 22b</li> <li>runtime_clustering_algorithm.csv: Runtime analysis of different clustering algorithms, related to Supp. Fig. 22c</li> </ul>

opencc-by-4.0Jun 2023View details →

dryad32/100

Data from: Phenotypic rate and state are decoupled in response to river-to-lake transitions in cichlid fishes

Geographic access to isolated ecosystems is an important catalyst of adaptive radiation. Cichlid fishes repeatedly colonized rift, crater, and volcanic lakes from surrounding rivers. We test the "lake effect" on the phenotypic rate and state across 253 cichlid species. The rate of evolution was consistently higher (~10-fold) in lakes, and consistent across different dimensions of the phenotype. Rate shifts tended to occur coincident with or immediately following river-to-lake transitions, generally resulting in 2- to 5-fold faster rates than in the founding riverine lineage. By contrast, river- and lake-dwelling cichlids exhibit considerable overlap in phenotypes, generally with less disparity in lakes, but often different evolutionary optima. Taken together, these results suggest that lake radiations rapidly expand into niches largely already represented by ancestral riverine lineages, albeit in different frequencies. Lakes may provide ecological opportunity via ecological release (e.g., from predators/competitors), but need not be coupled with access to novel ecological niches.

opencc-zeroAug 2023View details →

dryad32/100

Data for: Genotypes selected for early and late avian lay date differ in their phenotype, but not fitness, in the wild

Global warming has shifted phenological traits in many species, but whether species are able to track further increasing temperatures depends on the fitness consequences of additional shifts in phenological traits. To test this, we conducted a genomic selection experiment on wild great tits from the Hoge Veluwe study population (The Netherlands) to obtain great tits (Parus major) with genotypes for extremely early and late egg lay dates and measured phenology and fitness of selected great tit females under wild conditions. Females with early genotypes advanced lay dates relative to females with late genotypes, but not relative to non-selected females. Females with early and late genotypes did not differ in the number of fledglings produced, in line with the weak effect of lay date on the number of fledglings produced by non-selected females in the years of the experiment. Our study is the first application of genomic selection in the wild and led to an asymmetric phenotypic response that indicates the presence of constraints toward early, but not late, lay dates.

opencc-zeroAug 2023View details →

ClinicalTrials.gov32/100

Data Clustering Study With Artificial Intelligence and Phenotyping of Patients With Acute Pulmonary Embolism

ClinicalTrials.gov study NCT06183944. IPD Sharing: NO. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →

dryad32/100