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92 results for “network analyses”
Data and code for: Generation and applications of simulated datasets to integrate social network and demographic analyses
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Data from: Three-dimensional trajectories and network analyses of group behaviour within chimney swift flocks during approaches to the roost
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Data from: Network analyses support the role of prey preferences in shaping resource use patterns within five animal populations
Individual variation is an inherent aspect of animal populations and understanding the mechanisms shaping resource use patterns within populations is crucial to comprehend how individuals partition resources. Theory predicts that differences in prey preferences among consumers and/or differences in the likelihood of adding new resources to their diets are key mechanisms underlying intrapopulation variation in resource use. We developed network models based on optimal diet theory that simulate how individuals consume resources under varying scenarios of individual variation in prey preferences and in the willingness of consuming alternate resources. We then investigated how the structure of individual–resource networks generated under each model compared to the structure of observed networks representing five classical examples of individual diet variation. Our results support the notion that, for the studied populations, individual variation in prey preferences is the major factor explaining patterns in individual–resource networks. In contrast, variation in the willingness of adding prey does not seem to play an important role in shaping patterns of resource use. Individual differences in prey preferences in the studied populations may be generated by complex behavioral rules related to cognitive constraints and experience. Our approach provides a pathway for mapping foraging models into network patterns, which may allow determining the possible mechanisms leading to variation in resource use within populations.
Networks files of main scenarios analysed in "Distributed photovoltaics provides key benefits for a highly renewable European energy system"
<p>This repository contains the network files (.nc) of the main scenarios (A, B, C, and D) used for analysis in the paper. The code for reproducing these files plus other network files used for sensitivity analysis plus the Jupyter notebooks used for creating all the figures in the paper are available at: https://github.com/Parisra/Distributed-PV-paper </p>
Data from: Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas
Landscape connectivity is essential for maintaining viable populations, particularly for species restricted to fragmented habitats or naturally arrayed in metapopulations and facing rapid climate change. The importance of assessing both structural connectivity (the physical distribution of favorable habitat patches) and functional connectivity (how species move among habitat patches) for managing such species is well understood. However, the degree to which functional connectivity for a species varies among landscapes, and the resulting implications for conservation, have rarely been assessed. We used a landscape genetics approach to evaluate resistance to gene flow and, thus, to determine how landscape and climate-related variables influence gene flow for American pikas (Ochotona princeps) in eight federally managed sites in the western United States. We used those empirically-derived, individual-based landscape resistance models in conjunction with predictive occupancy models to generate patch-based network models describing functional landscape connectivity. Metareplication across landscapes enabled identification of limiting factors for dispersal that would not otherwise have been apparent. Despite the cool microclimates characteristic of pika habitat, south-facing aspects consistently represented higher resistance to movement, supporting the previous hypothesis that exposure to relatively high temperatures may limit dispersal in American pikas. We found that other barriers to dispersal included areas with a high degree of topographic relief, such as cliffs and ravines, as well as streams and distances greater than one to four kilometers depending on the site. Using the empirically-derived network models of habitat patch connectivity, we identified habitat patches that were likely disproportionately important for maintaining functional connectivity, areas in which habitat appeared fragmented, and locations that could be targeted for management actions to improve functional connectivity. We concluded that climate change, besides influencing patch occupancy as predicted by other studies, may alter landscape resistance for pikas, thereby influencing functional connectivity through multiple pathways simultaneously. Spatial autocorrelation among genotypes varied across study sites and was largest where habitat was most dispersed, suggesting that dispersal distances increased with habitat fragmentation, up to a point. This study demonstrates how landscape features linked to climate can affect functional connectivity for species with naturally fragmented distributions, and reinforces the importance of replicating studies across landscapes.
Back to the Past - Analysing Backporting Practices in Package Dependency Networks
<p>This is the replication package of our paper on the practice of backporting in package distributions.</p> <p>Please refer to the instructions in the README file.</p>
FIGURE. Median network analyses (MNA) of a subset of the C. trilobus aggregate (i.e. those in the clade A from Fig. 11) based on concatenated DNA sequence data from ITS, trnL-trnF and psbJ-petA. Stars and arrow indicate accessions discussed in the text. NI: North Island, SI: South Island. in Five new species of Corybas (Diurideae, Orchidaceae) endemic to New Zealand and phylogeny of the Nematoceras clade
FIGURE. Median network analyses (MNA) of a subset of the C. trilobus aggregate (i.e. those in the clade A from Fig. 11) based on concatenated DNA sequence data from ITS, trnL-trnF and psbJ-petA. Stars and arrow indicate accessions discussed in the text. NI: North Island, SI: South Island.
Replication data for: Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty
<p>Simulated host-parasite communities in Llopis‐Belenguer, C., J. A. Balbuena, I. Blasco‐Costa, A. Karvonen, V. Sarabeev, and J. Jokela. 2022. Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty. Ecology</p> <ul> <li>Full communities</li> </ul> <p>01_full_communities.RDS</p> <ul> <li>Resampled communities affected by host sampling completeness. From 90% to 10% of host sampling completeness every 10% steps</li> </ul> <p>02_resampled_sampling_completeness_90.RDS, </p> <p>03_resampled_sampling_completeness_80.RDS, </p> <p>04_resampled_sampling_completeness_70.RDS, </p> <p>05_resampled_sampling_completeness_60.RDS, </p> <p>06_resampled_sampling_completeness_50.RDS, </p> <p>07_resampled_sampling_completeness_40.RDS, </p> <p>08_resampled_sampling_completeness_30.RDS, </p> <p>09_resampled_sampling_completeness_20.RDS, </p> <p>10_resampled_sampling_completeness_10.RDS</p> <ul> <li>Resampled communities affected by parasite taxonomic resolution at all levels of host sampling completeness. From 90% to 10% of parasite taxonomic resolution and from 100% to 10% of host sampling completeness every 10% steps</li> </ul> <p>11_resampled_taxonomic_resolution_90_sampling_completeness_100-10.RDS, </p> <p>12_resampled_taxonomic_resolution_80_sampling_completeness_100-10.RDS, </p> <p>13_resampled_taxonomic_resolution_70_sampling_completeness_100-10.RDS, </p> <p>14_resampled_taxonomic_resolution_60_sampling_completeness_100-10.RDS, </p> <p>15_resampled_taxonomic_resolution_50_sampling_completeness_100-10.RDS, </p> <p>16_resampled_taxonomic_resolution_40_sampling_completeness_100-10.RDS, </p> <p>17_resampled_taxonomic_resolution_30_sampling_completeness_100-10.RDS, </p> <p>18_resampled_taxonomic_resolution_20_sampling_completeness_100-10.RDS, </p> <p>19_resampled_taxonomic_resolution_10_sampling_completeness_100-10.RDS</p> <p> </p>
Supplementary information files: Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows
<p>Supplementary information files: Gene co-expression network and differential expression analyses of subcutaneous white adipose tissue reveal novel insights into the pathological mechanisms underlying ketosis in dairy cows</p>
Data from: Network analyses support the role of prey preferences in shaping resource use patterns within five animal populations
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Network of Dmo2p interactors through mass spectrometry analyses of proteins differentially purified from the PC beads in the DMO2-PC strain
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Data from: Replicated landscape genetic and network analyses reveal wide variation in functional connectivity for American pikas
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Generalist plants are more competitive and more functionally similar to each other than specialist plants: insights from network analyses
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Dataset: A methodological framework to analyse determinants of host-microbiota networks, with an application to the relationships between Daphnia magna's gut microbiota and bacterioplankton
<p>Dataset and R codes for the publication "A methodological framework to analyse determinants of host-microbiota networks, with an application to the relationships between <em>Daphnia magna</em>’s gut microbiota and bacterioplankton"</p>
The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
<p>Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In grouping-event based networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analysing and interpreting their own network data using these methods.</p>
Molecular ecological network analyses: An effective conservation tool for the assessment of biodiversity, trophic interactions, and community structure
<p>Global biodiversity is threatened by the anthropogenic restructuring of animal communities, which rewires species interaction networks in real-time as individuals are extirpated or introduced. Conservation science and adaptive ecosystem management demands more rapid, quantitative, and non-invasive technologies for robustly capturing changing biodiversity and quantifying species interactions. Here we develop molecular ecological network analyses (MENA) as an ecosystem assessment tool to address these needs. To construct the ecological network, we used environmental DNA from feces to identify the plant and mammal diet of two carnivores: puma (<i>Puma concolor</i>) and bobcat (<i>Lynx rufus</i>); two omnivores: coyote (<i>Canis latrans</i>) and gray fox (<i>Urocyon cinereoargenteus</i>); and two herbivores: black-tailed deer (<i>Odocoileus hemionus</i>) and black-tailed jackrabbit (<i>Lepus californicus)</i> in a well-studied Californian reserve<i>. </i>To evaluate MENA as a comprehensive biodiversity tool, we applied our framework to identify the structure of the network, patterns of trophic interactions, key species, and to assess its utility in capturing the biodiversity of the area. The high dietary taxonomic resolution enabled the assessment of species diversity, niche breadth and overlap. The network analysis revealed a dense ecological network with a high diversity of weakly connected species and a community that is highly modular and non-nested. The significant prevalence of tri-trophic chain and exploitative competition patterns indicates (i) the removal or reintroduction of a top predator would trigger a trophic cascade within this community, directly affecting their prey and indirectly the plant communities, and (ii) the potential impact of indirect effects between two predators that consume the same prey. These results suggest that the recent resurgence of puma in the study area may impact the herbaceous and woody vegetation and the population size of other predators. This effect of fluctuating predator populations and plant communities could be predicted through MENA's fine-scale assessment of the diet selection and the identified keystone species. Although just using a subset of species, MENA more rapidly, accurately, and effectively captured the broader biodiversity of the area in comparison to other methodologies. MENA reconstructed and unveiled the hidden complexity in trophic structure and interaction networks within the community, providing a promising toolkit for biodiversity and ecosystem management.</p>
Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)
Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.
Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)
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Data from: Network analyses reveal intra- and interspecific differences in behaviour when passing a complex migration obstacle
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The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
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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.