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580 results for “pattern analysis”
FIG. 7 in An analysis of the sculptural pattern of the shell in Caribbean members of Chicoreus (Siratus) Jousseaume, 1880 (Gastropoda, Muricidae), with description of a new species
FIG. 7. — Spiral sculpture (apertural view), Chicoreus (Siratus) guionneti n. sp; A, Martinique (coll. Garrigues), five whorls; B, Guadeloupe (coll. Lamy), six whorls. For A, the siphonal canal is not completely drawn. Abbreviations: see text. Scale bars: 5 mm.
Long-term coastal macrobenthic Community Trajectory Analysis reveals habitat-dependent stability patterns
<p>Long-term monitoring programs are fundamental to detecting changes in ecosystem health and understanding ecological processes. In the current context of increasing anthropogenic threats on marine ecosystems, understanding the dynamics and response of communities becomes essential. We used data collected over 14 years in the REBENT benthic coastal invertebrates monitoring program, at a regional scale in the North-East Atlantic, covering a total of 26 sites and 979 taxa. Four distinct habitats were studied: two biogenic habitats associated with foundation species in the intertidal and subtidal zones and two bare sedimentary habitats in the same respective tidal zones. We used Community Trajectory Analysis, a statistical approach that allows for quantitative measures and comparisons of temporal trajectories of ecosystems. We compared observed community trajectories to trajectories simulated under a non-directional null model in order to better understand the dynamics of the communities, their potential drivers, and the role of the studied habitats in these dynamics. Despite strong differences in the community compositions between sites and habitats, the communities followed non-directional dynamics during the 14 years monitored, which suggested stability at the regional scale. However, the shape, size, and direction of the trajectories of benthic communities were more similar within than among habitats, also suggesting the influence of the nature of the habitat on community dynamics. Results showed a higher variability in community composition in the first years of the monitoring in the intertidal bare habitat and confirmed the role of biogenic habitats in maintaining temporal stability. They also highlighted the need to apprehend the role of transient and rare species and the scale of observation in temporal beta diversity analyses. Finally, our study confirmed the usefulness of Community Trajectory Analysis to link observed trajectory patterns to fundamental ecological processes.</p>
Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
<p>In medical, veterinary, and forensic entomology, the ease and affordability of image data acquisition have resulted in whole-image analysis becoming an invaluable approach for species identification. Krawtchouk moment invariants are a classical mathematical transformation that can extract local features from an image, thus allowing subtle species-specific biological variations to be accentuated for subsequent analyses. We extracted Krawtchouk moment invariant features from binarised wing images of 759 male fly specimens from the Calliphoridae, Sarcophagidae, and Muscidae families (13 species and a species variant). Subsequently, we trained the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) random forests classifier using linear discriminants derived from these features and inferred the species identity of specimens from the test samples. Five-fold cross validation results show a 98.56 ± 0.38% (standard error) mean identification accuracy at the family level, and a 91.04 ± 1.33% mean identification accuracy at the species level. The mean F1-score of 0.89 ± 0.02 reflects good balance of precision and recall properties of the model. The present study consolidates findings from previous small pilot studies of the usefulness of wing venation patterns for inferring species identities. Thus, the stage is set for the development of a mature data analytic ecosystem for routine computer image-based identification of fly species that are of medical, veterinary, and forensic importance.</p>
Dissecting glial scar formation by spatial point pattern and topological data analysis
<p>These data were generated by the Laboratory of Neurovascular Interactions (https://elalilab.com/) at University Laval (Quebec, Canada), and reported in "Dissecting glial scar formation by spatial point pattern and topological data analysis". </p> <p>Please refer to the Open Science Framework (OSF) repository (https://osf.io/3vg8j/) or GitHub (https://github.com/elalilab/GlialScar_PPA-TDA_2022) to see the processing pipeline.</p> <p><strong>AUTHORS</strong><br> Manrique-Castano, Daniel; Bhaskar, Dhananjay; ElAli, Ayman</p> <p><strong>KEYWORDS</strong><br> Stroke, cerebral ischemia, brain injury, glial scar, reactive astrocytes, reactive microglia, </p> <p><br> <strong>1. STUDY DESCRIPTION </strong> <br> This research provides a quantitative analysis of reactive glia and glial scar formation in a mouse model of cerebral ischemia. The dataset in this repository consists of raw widefield microscopy images from healthy and ischemic animals. </p> <p><strong>2. EXPERIMENTAL CONDITIONS</strong><br> Six-month-old C57BL/6 mice were subjected to 30 minutes of cerebral ischemia by middle cerebral artery occlusion (MCAO). Brains were harvested at 5, 15, and 30 days post-ischemia (DPI) (see 10.5281/zenodo.3559570). 5 sham animals were included as controls. The full protocol for brain harvesting is available at 10.17504/protocols.io.4r3l27q5pg1y/v1. Brain sections were stained with NeuN, Gfap, and Iba1 antibodies to detect neurons and reactive glia after injury. Full protocol available at 10.17504/protocols.io.yxmvmk94og3p/v1 <br> <br> <strong>3. FILE DESCRIPTION</strong></p> <p><strong>- GT5X_Gfap_Iba1_NeuN.rar: </strong>Contain widefield (5x magnification) .tif images grouped by animals (5-7 images per animal; see research article for further details). The images were taken with the following parameters.</p> <p>Objective: Fluar 5x/0.25 M27<br> Scaling per pixel: 1.300 x 1.300 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 3 s<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 4 s<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 1 s<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 50 ms</p> <p>We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_5x_GenerateTiffs.jim.</p> <p><strong>- GT10X_Gfap_Iba1_NeuN.rar:</strong> Contain a single widefield (10x magnification) .tif image per animal at the level of the MCA territory (see research article for further details). The images were taken with the following parameters.</p> <p>Objective: ECM paln-NeoFluar 10x/0.30 M27<br> Scaling per pixel: 0.45 x 0.45 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 200 ms<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 250 ms<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 100 ms<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 10 ms</p> <p><br> We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_10x_GenerateTiffs.jim.<br> <br> For 5x and 10x images, the following naming strings apply:</p> <p>GT5x: Research project identifier indicating the magnification<br> M01(n): Animal ID<br> 5D(n): Days post-ischemia. 0D refers to healthy (naive) animals. <br> Scene1(n): Bregma level. Scene 1 corresponds to the most anterior area sampled, while Scene 6 or 7 is the most posterior.</p> <p><strong>- PointPatterns_10x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises a horizontal ROI from the ventricular area to the outer border of the dorsolateral cerebral cortex. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_10x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- PointPatterns_5x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises the ischemic hemisphere. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_5x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- QupathProjects_5x.rar: </strong>QuPath project folder for 5x images (GT5X_Gfap_Iba1_NeuN.rar). Each subfolder (per animal) contains the necessary files to import annotations (alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, and NeuN folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" in each folder opens the QuPath project in QuPath and reads the classifiers and data folders. Each folder also contains "_Alignement.json" and "_Registration_json" files generated during the alignment and annotation procedures in ABBA. However, when the route of the source images is changed, the plugin does not allow rerouting, and the files are of no practical use. The issue has been reported to the ABBA Github repository. </p> <p><strong>- QupathProjects_10x.rar:</strong> QuPath project folder for 10x images (GT5X_Gfap_Iba1_NeuN.rar). The folder contains the necessary files to import annotations (Alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, NeuN, and DAPI folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" opens the QuPath project in QuPath and reads the classifiers and data folders. </p>
Data and analysis scripts for: Co-occurrence patterns at four spatial scales implicate reproductive processes in shaping community assembly in clovers
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Long-term coastal macrobenthic Community Trajectory Analysis reveals habitat-dependent stability patterns
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Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
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Comparative analysis of gut microbiome of mangrove brachyuran crabs revealed patterns of phylosymbiosis and codiversification
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A moving window analysis for exploring landscape and geologic controls on spatial patterning of streambank groundwater discharge
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Explaining global variation in the latitudinal diversity gradient: Meta-analysis confirms known patterns and uncovers new one
This dataset is also available on the Dryad Digital Repository (link: https://doi.org/10.5061/dryad.rg5rd). The code is also available on GitHub (link: https://github.com/nlkinlock/LDGmeta-analysis). This dataset was created to explore patterns in biodiversity across latitude. The pattern of increasing biological diversity from high latitudes to the equator [latitudinal diversity gradient (LDG)] has been recognized for greater than 200 years. Empirical studies have documented this pattern across many different organisms and locations. In order to quantify the evidence for the global LDG and the associated spatial, taxonomic and environmental factors, a systematic review, followed by a meta-analysis of the resulting dataset, were carried out. This dataset contains a large number of individual LDGs that have been published in the 14 years since Hillebrand's ground‐breaking meta‐analysis of the LDG.
Data from: Phylogenomic analysis of Wolbachia strains reveals patterns of genome evolution and recombination
<p><i>Wolbachia</i> are widespread intracellular bacteria that mediate many important biological processes in arthropod species. In this study, we identified 210 conserved single-copy genes in 33 genome-sequenced <i>Wolbachia</i> strains in the A, B, C, D, E and F supergroups. Phylogenomic analysis with these core genes indicate that all 33 <i>Wolbachia</i> strains maintain the supergroup relationship classified previously based on the multilocus sequence typing (MLST) genes. Using an interclade recombination screening method, 14 inter-supergroup recombination events were discovered in six genes (2.9%) among 210 single copy orthologs. This finding suggests a relatively low frequency of intergroup recombination. Interestingly, they have occurred not only between A and B supergroups (9 events), but also between A and E supergroups (5 events). Maintenance of such transfers suggests possible roles in <i>Wolbachia</i> infection related functions. Comparisons of strain divergence using the five genes of the MLST system show a high correlation (Pearson correlation coefficient r = 0.98) between MLST and whole genome divergences, indicating that MLST is a reliable method for identifying related strains when whole genome data are not available. The phylogenomic analysis and the identified core gene set in our study will serve as a valuable foundation for strain identification and the investigation of recombination and genome evolution in <i>Wolbachia</i>.</p>
Analysis of migration patterns of western marsh harriers using GPS tracking data
<p>This repository contains analysis code for Vansteelant et al. (2020, <a href="https://doi.org/10.1007/s10336-020-01785-6">https://doi.org/10.1007/s10336-020-01785-6</a>). See the <code>README.md</code> for more information.</p>
Data from: Beta diversity patterns of bats in the Atlantic Forest: how does the scale of analysis affect the importance of spatial and environmental factors?
<p>Aim: Environmental and spatial factors are broadly recognized as important predictors of beta diversity patterns. However, the scale at which beta diversity patterns are evaluated will affect the outcoming results. For example, studies at larger scales will usually find spatial processes as the main predictor of beta diversity patterns. In this study we evaluate how beta diversity patterns change when analyses are conducted at different scales by reducing the scale of analysis in a hierarchical manner.</p> <p>Taxon: Chiroptera.</p> <p>Location: Atlantic Forest biome.</p> <p>Methods: Information on the occurrence of 59 bat species were obtained from the Atlantic Bats and Species Link database. We partitioned beta diversity into its two components (nestedness and turnover), and calculated these indexes hierarchically: the biome in its entirety (all ecoregions); between larger regions (north, central and south); and between ecoregions within each region. We performed a Generalized Dissimilarity Model (GDM) to identify and predict the turnover of bat species in the Atlantic Forest based on geo-climatic predictors. We obtained 19 geo-climatic data from AMBDATA, an environmental dataset based on different data sources commonly used in species distribution modeling.</p> <p>Results: We found that turnover was the main component influencing a latitudinal gradient when the biome was analysed in its entirety. However, when the scale of the analysis was reduced, we found that species loss (nestedness component) had a large effect in determining beta diversity dissimilarity. We also found that nestedness was the main pattern explaining beta diversity dissimilarity along a longitudinal gradient.</p> <p>Main conclusions: Beta diversity patterns changed with the scale of analysis, which indicates that bat species composition does not follow the same pattern throughout the Atlantic Forest. This corroborates the importance of analysing beta diversity patterns at different scales in order to understand how environmental dissimilarity across geographic space can influence species distribution patterns.</p>
Short-Tandem-Repeat (STR) marker set for Eurasian lynx for article: Genetic analysis indicates spatial-dependent patterns of sex-biased dispersal in Eurasian lynx in Finland
<p>Conservation and management of large carnivores requires knowledge of female and male dispersal. Such information is crucial to evaluate the population's status and thus management actions. This knowledge is challenging to obtain, often incomplete and contradictory at times. The size of the target population and the methods applied can bias the results. Also, population history and biological or environmental influences can affect dispersal on different scales within a study area. We have genotyped Eurasian lynx (180 males and 102 females, collected 2003-2017) continuously distributed in southern Finland (~23,000 km<sup>2</sup>) using 21 short tandem repeats (STR) loci and compared statistical genetic tests to infer local and sex-specific dispersal patterns within and across genetic clusters as well as geographic regions. We tested for sex-specific substructure with individual-based Bayesian assignment tests and spatial autocorrelation analyses. Differences between the sexes in genetic differentiation, relatedness, inbreeding, and diversity were analysed using population-based AMOVA, F-statistics, and assignment indices. Our results showed two different genetic clusters that were spatially structured for females but admixed for males. Similarly, spatial autocorrelation and relatedness was significantly higher in females than males. However, we found weaker sex-specific patterns for the Eurasian lynx when the data were separated in three geographical regions than when divided in the two genetic clusters. Overall, our results suggest male-biased dispersal and female philopatry for the Eurasian lynx in Southern Finland. The female genetic structuring increased from west to east within our study area. In addition, detection of male-biased dispersal was dependent on analytical methods utilized, on whether subtle underlying genetic structuring was considered or not, and the choice of population delineation. Conclusively, we suggest using multiple genetic approaches to study sex-biased dispersal in a continuously distributed species in which population delineation is difficult.</p>
Data from: A new digital method of data collection for spatial point pattern analysis in grassland communities
<p>A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of <i>Stipa</i><i> grandis</i> in grazed and ungrazed <i>S. grandis</i> communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of <i>S. grandis</i> by using the pair correlation function <i>g</i>(<i>r</i>) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of <i>S. grandis</i> were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.</p>
Data from: Explaining global variation in the latitudinal diversity gradient: meta-analysis confirms known patterns and uncovers new ones
Aim: The pattern of increasing biological diversity from high latitudes to the equator [latitudinal diversity gradient (LDG)] has been recognized for > 200 years. Empirical studies have documented this pattern across many different organisms and locations. Our goal was to quantify the evidence for the global LDG and the associated spatial, taxonomic and environmental factors. We performed a meta-analysis on a large number of individual LDGs that have been published in the 14 years since Hillebrand's ground-breaking meta-analysis of the LDG, using meta-analysis and meta-regression approaches largely new to the fields of ecology and biogeography. Location: Global. Time period: January 2003–September 2015. Major taxa studied: Bacteria, protists, plants, fungi and animals. Methods: We synthesized the outcomes of 389 individual cases of LDGs from 199 papers published since 2003, using hierarchical mixed-effects meta-analysis and multiple meta-regression. Additionally, we re-analysed Hillebrand's original dataset using modern methods. Results: We confirmed the generality of the LDG, but found the pattern to be weaker than was found in Hillebrand's study. We identified previously unreported variation in LDG strength and slope across longitude, with evidence that the LDG is strongest in the Western Hemisphere. Locational characteristics, such as habitat and latitude range, contributed significantly to LDG strength, whereas organismal characteristics, including taxonomic group and trophic level, did not. Modern meta-analytical models that incorporate hierarchical structure led to more conservative and sometimes contrasting effect size estimates relative to Hillebrand's initial analysis, whereas meta-regression revealed underlying patterns in Hillebrand's dataset that were not apparent with a traditional analysis. Main conclusions: We present evidence of global latitudinal, longitudinal and habitat-based patterns in the LDG, which are apparent across both marine and terrestrial realms and over a broad taxonomic range of organisms, from bacteria to plants and vertebrates.
Dataset from : Unsteady Analysis of a Pulsating Alternate Flow Pattern in a Radial Vaned Diffuser.
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Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses
<p>This repository houses a comprehensive collection of data and resources from the study "Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses." Led by Dr. Suresh Neethirajan at Mooanalytica, Department of Agriculture & Aquaculture, Faculty of Agriculture & Computer Science, Dalhousie University, this research represents a significant foray into the field of poultry ethology and welfare monitoring using advanced machine learning techniques.</p> <p><strong>Key Components of the Repository</strong></p> <ol> <li> <p><strong>Experimental Audio Data</strong></p> <ul> <li><strong>Control and Treatment Vocalizations</strong>: Audio recordings of laying hens under two different stress conditions – sudden umbrella opening (Treatment 1) and simulated dog barking sounds (Treatment 2), along with control groups. The processed dataset is approximately 460 MB for the control group experimental data and about 2 GB for the 2 treatment group experimental data, capturing the nuanced responses of hens to these stressors.</li> <li><strong>Original Raw Data</strong>: The original, unprocessed audio data is around 9 GB in size. Though not included in the repository, it can be made available upon reasonable request.</li> </ul> </li> <li> <p><strong>Algorithm and Code Files</strong></p> <ul> <li><strong>CNN Feature Extraction and Classification Algorithms</strong> Python scripts used for the extraction of features from the audio data using Convolutional Neural Networks (CNN) and subsequent classification.</li> <li><strong>Supplementary Algorithms</strong> Additional code files that support the processing and analysis of the audio data.</li> </ul> </li> <li> <p><strong>MFCC Feature Dataset</strong></p> <ul> <li>An Excel file containing the 40 Mel Frequency Cepstral Coefficients (MFCC) features extracted from the vocalization data. This dataset provides a detailed spectral analysis of the hen's vocalizations, crucial for understanding their response to stress.</li> </ul> </li> </ol> <p><strong>Study Overview</strong></p> <p>This study aimed to classify and analyze the vocalization patterns of laying hens subjected to different stressors. Using a CNN model, the research identified distinct vocal patterns between control and treated groups, indicating unique vocal responses to different types of stressors. This study is pivotal in understanding the impact of environmental stressors on poultry welfare and behavior. The age of the chickens and the timing of stressor application were also critical factors influencing vocalization patterns.</p> <p><strong>Implications and Applications:</strong></p> <p>The findings from this study have significant implications for poultry welfare monitoring and management. By providing a non-invasive method to assess the well-being of chickens, this research contributes valuable insights into enhancing poultry management practices and welfare standards.</p> <p>The resources in this repository are intended for researchers, academicians, and professionals in animal behavior, veterinary science, and poultry management. We encourage the use of these data and tools for further research and practical applications in the field of precision (Digital) livestock farming and animal welfare.</p> <p>For any queries or requests related to the raw dataset, please contact Dr. Suresh Neethirajan.</p>
Kikuchi patterns for cNMF analysis.
<p><strong>Employing constrained non-negative matrix factorization for microstructure segmentation</strong></p> <p>Materials characterization using electron backscatter diffraction (EBSD) requires indexing the orientation of the measured region from Kikuchi patterns. The quality of Kikuchi patterns can degrade due to pattern overlaps arising from two or more orientations, in the presence of defects or grain boundaries. In this work we employ constrained non-negative matrix factorization to segment a microstructure with small grain misorientations,~\mbox{($<1\degree$)}, and predict the amount of pattern overlap. First we implement the method on mixed simulated patterns - that replicates a pattern overlap scenario, and demonstrate the resolution limit of pattern mixing or factorization resolution using a weight metric. Subsequently, we segment a single-crystal dendritic microstructure and compare the results with high resolution EBSD. By utilizing weight metrics across a low angle grain boundary we demonstrate how very small misorientations/low-angle grain boundaries can be resolved at a pixel level. Our approach constitutes a versatile and robust tool, complementing other fast indexing methods for microstructure characterization.</p>
Data from: Genetic analysis of red deer (Cervus elaphus) administrative management units in a human-dominated landscape - patterns of genetic diversity, population structure and gene flow
<p><span><span>Red deer (</span><span><em>Cervus elaphus</em></span><span>) throughout central Europe are</span> impacted by different anthropogenic activities including habitat fragmentation, selective hunting, and translocations<span>. This has substantial influences on genetic diversity and the long-term conservation of local populations of this species. Here we use genetic samples from 480 red deer individuals to assess the genetic diversity and differentiation of the 12 administrative management units located in Schleswig Holstein, the northernmost federal state in Germany. </span></span><span><span>We applied multiple analytical approaches and show that the history of local populations (i.e., translocations, culling of individuals outside of designated red deer zones, and anthropogenic infrastructures) has led to comparably low levels of genetic diversity. The mean expected heterozygosity was below 0.6 and we observed on average 4.2 alleles across 12 microsatellite loci. Effective population sizes below the recommended level of 50 were estimated for multiple local populations. </span></span><span><span>Our estimates of genetic structure and gene flow show that red deer in northern Germany are best described as a complex network of asymmetrically connected subpopulations, with high genetic exchange among some local populations and reduced connectivity of others. Genetic diversity was also correlated with population densities of neighboring management units. </span></span></p> <p><span><span>Based on these findings, we suggest that connectivity among existing management units needs to be considered in the practical management of the species, which means that some administrative management units should be managed together, while the effective isolation of other units needs to be mitigated.</span></span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.