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73 results for “Network robustness”
Data for: Prescribed fire increases plant-pollinator network robustness to losses of rare native forbs
<p>Restoration efforts often focus on changing the composition and structure of invaded plant communities, with two implicit assumptions: 1) functional interactions with species of other trophic levels, such as pollinators, will reassemble automatically when native plant diversity is restored; and 2) restored communities will be more resilient to future stressors. However, the impact of restoration activities on pollinator richness, plant-pollinator interaction network structure, and network robustness is incompletely understood. Leveraging a restoration chronosequence in Pacific Northwest prairies, we examined the effects of restoration-focused prescribed fire and native forb replanting on floral resources, pollinator visitation, and plant-pollinator network structure. We then simulated the effects of plant species loss/removal scenarios on secondary extinction cascades in the networks. Specifically, we explored three management-relevant plant loss scenarios (removal of an abundant exotic forb, removal of an abundant forb designated a noxious weed, and loss of the rarest native forb) and compared them to control scenarios. Pyrodiversity, proportion of area recently burned, and cumulative replanting effort (plugging and seeding) over the prior 10 years increased the abundance and diversity of floral resources, with concomitant increases in pollinator visitation and diversity. Pyrodiversity also decreased network connectance and nestedness, increased modularity, and buffered networks against secondary extinction cascades. Rare forbs contributed disproportionately to network robustness in less restored prairies, while removal of typical "problem" plants like exotic and noxious species had relatively small impacts on network robustness, particularly in prairies with a long history of restoration activities. Restoration actions aimed mainly at improving the diversity and abundance of pollinator-provisioning plants may also produce plant-pollinator networks with increased resilience to plant species losses.</p>
Data from:Spatio-temporal networks: reachability, centrality and robustness
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Data for: Prescribed fire increases plant-pollinator network robustness to losses of rare native forbs
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Data from: Rainforest fragmentation decreases the robustness of plant-frugivore interaction networks
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Data from: Urbanisation and agricultural intensification modulate plant-pollinator network structure and robustness
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Mapping trait versus species turnover reveals spatiotemporal variation in functional redundancy and network robustness in a plant‐pollinator community
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Social network structure is robust to parasite induced changes in contact behavior of domestic sheep
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Data from: Glacier retreat decreases mutualistic network robustness over spacetime
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Robustness of a meta‐network to alternative habitat loss scenarios
<p>Studying how habitat loss affects the tolerance of ecological networks to species extinction (i.e. their robustness) is key for our understanding of the influence of human activities on natural ecosystems. With networks typically occurring as local interaction networks interconnected in space (a meta-network), we may ask how the loss of specific habitat fragments affects the overall robustness of the meta-network. To address this question, for an empirical meta-network of plants, herbivores and natural enemies we simulated the removal of habitat fragments in increasing and decreasing order of area, age and connectivity for plant extinction and the secondary extinction of herbivores, natural enemies and their interactions. Meta-network robustness was characterized as the area under the curve of remnant species or interactions at the end of a fragment removal sequence. To pinpoint the effects of fragment area, age and connectivity, respectively, we compared the observed robustness for each removal scenario against that of a random sequence. The meta-network was more robust to the loss of old (i.e. long-fragmented), large, connected fragments than of young (i.e. recently fragmented), small, isolated fragments. Thus, young, small, isolated fragments may be particularly important to the conservation of species and interactions, while contrary to our expectations larger, more connected fragments contribute little to meta-network robustness. Our findings highlight the importance of young, small, isolated fragments as sources of species and interactions unique to the regional level. These effects may largely result from an unpaid extinction debt, in which case these fragments are likely to lose species over time. Yet, there may also be more long-lasting effects from cultivated lands (e.g. water, fertilizers and restricted cattle grazing) and network complexity in small, isolated fragments. Such fragments may sustain important biological diversity in fragmented landscapes, but maintaining their conservation value may depend on adequate restoration strategies.</p>
Agricultural intensification drives changes in hybrid network robustness by modifying network structure
<p>Within ecological communities, species engage in myriad interaction types, yet empirical examples of hybrid species interaction networks composed of multiple types of interactions are still scarce. A key knowledge gap is understanding how the structure and stability of such hybrid networks are affected by anthropogenic disturbance. Using 15,169 interaction observations, we constructed 16 hybrid herbivore-plant-pollinator networks along an agricultural intensification gradient to explore changes in network structure and robustness to local extinctions. We found that agricultural intensification led to declines in modularity but increases in nestedness and connectance. Notably, network connectance, a structural feature typically thought to increase robustness, caused declines in hybrid network robustness, but the directionality of changes in robustness along the gradient depended on the order of local species extinctions. Our results not only demonstrate the impacts of anthropogenic disturbance on hybrid network structure, but they also provide unexpected insights into the structure-stability relationship of hybrid networks.</p>
Data from: The impact of gene expression variation on robustness and evolvability of a developmental gene regulatory network
Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear), allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.
Data from: Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species
Selective logging is one of the major drivers of tropical forest degradation, causing important shifts in species composition. Whether such changes modify interactions between species and the networks in which they are embedded remain fundamental questions to assess the 'health' and ecosystem functionality of logged forests. We focus on interactions between lianas and their tree hosts within primary and selectively logged forests in the biodiversity hotspot of Malaysian Borneo. We found that lianas were more abundant, had higher species richness and different species compositions in logged than in primary forests. Logged forests showed heavier liana loads disparately affecting slow-growth tree species, which could exacerbate the loss of timber value and carbon storage already associated to logging. Moreover, simulation scenarios of host tree local species loss indicated that logging might decrease the robustness of liana-tree interaction networks if heavily infested trees (i.e. the most connected ones) are more likely to disappear. This effect is partially mitigated in the short term by the colonization of host trees by a greater diversity of liana species within logged forests, yet this might not compensate for the loss of preferred tree hosts in the long term. As a consequence, species interaction networks may show a lagged response to disturbance, which may trigger sudden collapses in species richness and ecosystem function in response to additional disturbances, representing a new type of "extinction debt".
Data from: Learning and robustness to catch-and-release fishing in a shark social network
Individuals can play different roles in maintaining connectivity and social cohesion in animal populations and thereby influence population robustness to perturbations. We performed a social network analysis in a reef shark population to assess the vulnerability of the global network to node removal under different scenarios. We found that the network was generally robust to the removal of nodes with high centrality. The network appeared also highly robust to experimental fishing. Individual shark catchability decreased as a function of experience, as revealed by comparing capture frequency and site presence. Altogether, these features suggest that individuals learnt to avoid capture, which ultimately increased network robustness to experimental catch-and-release. Our results also suggest that some caution must be taken when using capture–recapture models often used to assess population size as assumptions (such as equal probabilities of capture and recapture) may be violated by individual learning to escape recapture.
Data from: The robustness and restoration of a network of ecological networks
Understanding species' interactions and the robustness of interaction networks to species loss is essential to understand the effects of species' declines and extinctions. In most studies, different types of networks (such as food webs, parasitoid webs, seed dispersal networks, and pollination networks) have been studied separately. We sampled such multiple networks simultaneously in an agroecosystem. We show that the networks varied in their robustness; networks including pollinators appeared to be particularly fragile. We show that, overall, networks did not strongly covary in their robustness, which suggests that ecological restoration (for example, through agri-environment schemes) benefitting one functional group will not inevitably benefit others. Some individual plant species were disproportionately well linked to many other species. This type of information can be used in restoration management, because it identifies the plant taxa that can potentially lead to disproportionate gains in biodiversity.
Reproducibility and robustness of graph measures of the Associative-Semantic Network
<p>Matfiles and matlab scripts used to study the reproducibility and robustness of graph measures of the Associative-Semantic Network.</p>
PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
<p>Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in increasing the flexibility and reducing the computational cost of seismic modeling and inversion. We propose a novel implementation of PINN using frequency upscaling and neuron splitting, which allows the neural network model to grow in size as we increase the frequency while leveraging the information from the pre-trained model for lower-frequency wavefields, resulting in fast convergence to high-accuracy wavefield solutions. In this letter, we present the relevant dataset to the paper. </p>
Dataset (MATLAB format) from Chen Kang et al (2021) Modularity and robustness of frontal cortex networks. Cell, 184(14):3717-3730.
<p><strong>Summary</strong></p> <p>These experiments simultaneously measure neuronal responses from the two hemispheres of premotor cortex (anterior lateral motor cortex, ALM) of adult mice performing pole location discrimination with a short-term memory. We inactivate activity of one hemisphere (left or right) in some trials and both hemispheres in others. Data from 135 recording sessions are included in this release.</p> <p>This dataset contains data from 39 mice (age > P60). 9 VGAT-ChR2-EYFP mice (Jackson laboratory, JAX Stock#014548) and 30 PV-IRES-Cre (JAX Stock#008069) crossed to Rosa26-LSL-ReaChR, red-shifted channelrhodopsin reporter mice (JAX 24846), were used for simultaneous electrophysiology and photoinhibition. The experiments (including experiment methods) are described in the following publication:</p> <p><em>Chen, G., Kang, B., Lindsey, J., Druckmann, S., Li, N. (2021). Modularity and robustness of frontal cortical networks. Cell 184, 1-14. doi: 10.1016/j.cell.2021.05.026</em></p> <p> </p> <p><strong>How to cite the data</strong></p> <p>If you publish any work using the data, please cite the Chen et. al., (2021) publication above and also cite the dataset in the following recommended format:</p> <p>Chen G, Li N (2021); Data and simulations related to: Modularity and robustness of frontal cortical networks. Chen et al (2021) Cell, 184(14):3717-3730.</p> <p><a href="http://dx.doi.org/10.5281/zenodo.6713616">http://dx.doi.org/10.5281/zenodo.6713616</a></p> <p> </p> <p><strong>How to get started</strong></p> <p>Once downloaded</p> <p>1) unzip “<strong>analysis_scripts</strong>”</p> <p>2) unzip and combine all "<strong>data_structure_*.mat</strong>" and "<strong>meta_data_*.mat</strong>" files into a single folder “<strong>datafiles</strong>”.</p> <p> • <strong>data_structure_*.mat </strong>- contains raw spike data for one session.</p> <p> • <strong>meta_data_*.mat</strong> - contains the meta data information for one session.</p> <p> There is one "<strong>meta_data_*.mat</strong>" file for each "<strong>data_structure_*.mat</strong>"</p> <p>3) Run scripts within "<strong>analysis_scripts</strong>". Follow the instruction in the "<strong>code instruction</strong>".</p> <p> </p> <p><strong>Data analysis</strong></p> <p>The extracellular recording traces were band-pass filtered (300-6 kHz). Events that exceeded an amplitude threshold (4 standard deviations of the background) were subjected to spike sorting to extract single units. Spike sorting was either manual (Guo et al., 2014b) or using Kilosort2 (https://www.github.com/MouseLand/Kilosort2) (Pachitariu et al., 2016) followed by manually curated with the Phy 2.0 beta 1 GUI (https://github.com/cortex-lab/phy) (Rossant et al., 2016) and manual inspection (Guo et al., 2014a). Spike widths were computed as the trough-to-peak interval in the mean spike waveform. Units with spike width < 0.35 ms were defined as fast-spiking (FS) neurons and units with spike widths > 0.45 ms as putative pyramidal neurons. Units with intermediate values (0.35 - 0.45 ms) were excluded. We concentrated our analyses on the putative pyramidal neurons.</p>
Datasets for reproducing "Robust Modelling of Internet Delay and Smart Monitoring Schemes for the Automation of Overlay Networks"
<p>This upload contains the datasets necessary to reproduce the figures and the results of my PhD thesis titled "<a href="https://tel.archives-ouvertes.fr/tel-03666771/document">Robust Modelling of Internet Delay and Smart Monitoring Schemes for the Automation of Overlay Networks</a>" (2020).</p> <p>These datasets are derived from public sources: <a href="https://www.caida.org/projects/manic/">CAIDA MANIC</a> and <a href="https://atlas.ripe.net/">RIPE Atlas</a>.</p>
Agricultural intensification drives changes in hybrid network robustness by modifying network structure
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Environmental robustness of the global yeast genetic interaction network
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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.