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364 results for “Network interaction”
Data from: Interaction rewiring and the rapid turnover of plant-pollinator networks
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Wildfire severity alters drivers of interaction beta-diversity in plant-bee networks
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Interaction networks of nectar-feeding bats and plants in central Mexico
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Bat-flower interaction networks in Caatinga reveal generalized associations and temporal stability
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Individual-based networks reveal the highly skewed interactions of a frugivore mutualist with individual plants in a diverse community
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Data from: Parasites alter interaction patterns in fish social networks
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Data from: Estimated missing interactions change the structure and alter species roles in one of the world’s largest seed-dispersal networks
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Successional and phenological effects on plant-floral visitor interaction networks of a tropical dry forest
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Legacy effects of seed dispersal mechanisms shape the spatial interaction network of plant species in Mediterranean forests
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Changes in the structure of seed dispersal networks when including interaction outcomes from both plant and animal perspectives
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Siderophore synthetase-receptor gene coevolution reveals habitat and pathogen-specific bacterial iron interaction networks
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Data from: From structure to function in mutualistic interaction networks: topologically important frugivores have greater potential as seed dispersers
1. Networks of mutualistic interactions between animals and plants are considered a pivotal part of ecological communities. However, mutualistic networks are rarely studied from the perspective of species-specific roles, and it remains to be established whether those animal species more relevant for network structure also contribute more to the ecological functions derived from interactions. 2. Here, we relate the contribution to seed dispersal of vertebrate species with their topological role in frugivore-plant interaction networks. For one year in two localities with remnant patches of Colombian tropical dry forest, we sampled abundance, morphology, behavior, and fruit consumption from fleshy-fruited plants of various frugivore species. 3. We assessed the network topological role of each frugivore species by integrating their degree of generalization in interactions with plants with their contributions to network nestedness and modularity. We estimated the potential contribution of each frugivore species to community-wide seed dispersal, on the basis of a set of frugivore ecological, morphological and behavioral characteristics important for seed dispersal, together with frugivore abundance and frugivory degree. 4. The various frugivore species showed strong differences in their network structural roles, with generalist species contributing the most to network modularity and nestedness. Frugivores also showed strong variability in terms of potential contribution to seed dispersal, depending on the specific combinations of frugivore abundance, frugivory degree and the different traits and behaviors. 5. For both localities, the seed dispersal potential of a frugivore species responded positively to its contribution to network structure, evidencing that the most important frugivore species in the network topology were also those making the strongest contribution as seed dispersers. Contribution to network structure was correlated with frugivore abundance, diet, and behavioral characteristics. This suggests that the species-level link between structure and function is due to the fact that the occurrence of frugivore-plant interactions depends largely on the characteristics of the frugivore involved, which also condition its ultimate role in seed dispersal. 22-May-2020
Large-scale metabolic interaction network of the mouse and human gut microbiota
<p>The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities.</p>
Uncovering structural features that underlie coexistence in an invaded woody plant community with interaction networks at multiple life stages
<p>Understanding the patterns of competitive and facilitative interactions within and among species in plant communities is a central goal of plant ecology, because these patterns determine species coexistence and community dynamics. Network theory provides tools that allow these patterns to be quantified, and can provide greater understanding of important community properties, including community stability, than can documenting pairwise species interactions.</p> <p>I characterized the interactions of multiple, co-occurring invasive and native species in an old field woody plant community to build plant interaction networks at two different life stages. With the goal of identifying structural features that may operate to maintain species coexistence, I characterized the architecture of these networks at multiple scales: the entire network, the substructures that compose the network, and species' roles within substructures.</p> <p>I found that species-level pairwise interactions alone did not provide an accurate or sufficiently detailed picture of community structure. Rather, using a network approach, I identified substructures that have the potential to promote and hinder species coexistence in interactions among seedlings. Characterizing the nuances of network substructures was illuminating, as the size of the substructures and the pattern of interaction intensities within substructures influence the expected effects on species coexistence. Including interactions at multiple life stages was also important; the seedling species that benefited most from the nested structure of facilitative interactions with adults occupied subordinate roles in substructures with other seedlings. This role reversal at different life stages is a potential factor promoting coexistence in the community. Last, the network framework was useful for comparing species' roles between native and invasive members of the community, and the three invasive species in this system had different, life-stage dependent strategies in interactions with co-occurring plants.</p> <p><em>Synthesis</em>. The interplay of network architecture and substructures within plant communities and among plants at different life stages is important for understanding species coexistence. In the plant community characterized in this study, there were several features that may promote coexistence, and these features were not observable in interactions within a single life stage or when considering pairwise interactions independently.</p>
Data from: Seasonal dynamics of flock interaction networks across a human-modified landscape in lowland Amazonian rainforest
<p><span>Although lowland tropical rainforests were once widely believed to be the archetype of stability, seasonal variation exists. In these environments, seasonality is defined by rainfall, leading to a predictable pattern of biotic and abiotic changes. Only the full annual cycle reveals niche breadth, yet most studies of tropical organisms ignore seasonality, thereby underestimating realized conditions. If human-modified habitats display more seasonal stress than intact habitats, then ignoring seasonality will have particularly important repercussions for conservation. We examined the seasonal dynamics of Amazonian mixed-species flocks—an important species interaction network—across three habitats with increasing human disturbance. We quantified seasonal space use, species richness and attendance, and four ecological network metrics for flocks in primary forest, small forest fragments, and regenerating secondary forest in central Amazonia. Our results indicate that, even in intact, lowland rainforest, mixed-species flocks exhibit seasonal differences. </span>During the dry season, flocks included more species, generally ranged over larger areas, and displayed network structures that were less complex and less cohesive. We speculate that because most flocking species nest during the dry season—a time of reduced arthropod abundance—flocks are simultaneously constrained by these two competing pressures. Moreover, these seasonal differences were most pronounced in forest fragments and secondary forest, habitats that are less buffered from the changing seasons. <span>Our results suggest that seasonality influences the conservation value of human-modified habitats, raising important questions about how rainforest organisms will cope with an increasingly unstable climate.</span></p>
Data from: Incorporating alternative interaction modes, forbidden links and trait-based mechanisms increases the minimum trait dimensionality of ecological networks
<ol> <li>Individual-level traits mediate interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits that characterise ecological networks, i.e. their 'minimum dimensionality'. Existing methods for estimating minimum dimensionality often lack three features associated with increased trait numbers: alternative interaction modes (e.g. feeding strategies such as active vs. sit-and-wait feeding), trait-mediated 'forbidden links' and a mechanistic description of interactions. Omitting these features can underestimate the trait numbers involved, and therefore, minimum dimensionality. We develop a 'minimum mechanistic dimensionality' measure, accounting for these three features.</li> <li>The only input our method requires is the network of interaction outcomes. We assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between interacting species. For example, if a predator feeds upon a prey species via a typical predation mode, in each step of the predation sequence the predator's performance must be greater than the prey's. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The number of traits required for a feasible system of inequalities provides our minimum dimensionality estimate.</li> <li>We applied our method to 658 published empirical ecological networks including primary consumption, predator–prey, parasitism, pollination, seed dispersal and animal dominance networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), 'forbidden interactions' as trait-mediated interaction outcomes (92%), or a mechanistic perspective (81%), compared to estimates missing these features. Additionally, we tested minimum dimensionality estimates on simulated networks with known dimensionality. Our method typically estimated a higher minimum dimensionality, closer to the actual dimensionality, while avoiding the overestimation associated with a previous method.</li> <li>Our method can reduce the risk of omitting traits involved in different interaction modes, in failure outcomes, or mechanistically. More accurate estimates will allow us to parameterise models of theoretical networks with more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics.</li> </ol>
A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions
<p>Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ~380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands, and binding partners for 'orphan' receptors. New PPIs include proteins expressed on multiple cell types, and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance, and may offer new therapeutic drug targets.</p>
Data from: Differential effects of fertilisers on pollination and parasitoid interaction networks
Grassland fertilisation drives non-random plant loss resulting in areas dominated by perennial grass species. How these changes cascade through linked trophic levels, however, is not well understood. We studied how grassland fertilisation propagates change through the plant assemblage into the plant-flower visitor, plant-leaf miner and leaf miner-parasitoid networks using a year's data collection from a long-term grassland fertiliser application experiment. Our experiment had three fertiliser treatments each applied to replicate plots 15 m2 in size: mineral fertiliser, farmyard manure, and mineral fertiliser and farmyard manure combined, along with a control of no fertiliser. The combined treatment had the most significant impact, and both plant species richness and floral abundance decreased with the addition of fertiliser. While insect species richness was unaffected by fertiliser treatment, fertilised plots had a significantly higher abundance of leaf miners and parasitoids and a significantly lower abundance of bumblebees. The plant-flower visitor and plant-herbivore networks showed higher values of vulnerability and lower modularity with fertiliser addition, while leaf miner-parasitoid networks showed a rise in generality. The different groups of insects were impacted by fertilisers to varying degrees: while the effect on abundance was the highest for leaf miners, the vulnerability and modularity of flower visitor networks was the most affected. The impact on the abundance of leaf miners was positive and three times higher than the impact on parasitoids, and the impact on bumblebee abundance was negative and double the magnitude of impact on flower abundance. Overall our results show that while insect species richness was unaffected by fertilisers, network structure changed significantly as the replacement of forbs by grasses resulted in changes in relative abundance across trophic levels, with the direction of change depending on the type of network. By studying multiple networks simultaneously, we were able to rank the relative impact of habitat change on the different groups of species within the community. This provided a more holistic picture of the impact of agricultural intensification and provides useful information when deciding on priorities for mitigation. 01-Oct-2020
Data for: Infectious disease and sickness behaviour: tumour progression affects interaction patterns and social network structure in wild Tasmanian devils
<p>Infectious diseases, including transmissible cancers, can have a broad range of impacts on host behaviour, particularly in the latter stages of disease progression. However, the difficulty of early diagnoses makes the study of behavioural influences of disease in wild animals a challenging task. Tasmanian devils (<i>Sarcophilus harrisii</i>) are affected by a transmissible cancer, devil facial tumour disease (DFTD), in which tumours are externally visible as they progress. Using telemetry and mark-recapture data sets, we quantify the impacts of cancer progression on the behaviour of wild devils by assessing how interaction patterns within the social network of a population change with increasing tumour load. DFTD negatively influences devils' likelihood of interaction within their network, an effect which increases with increasing tumour load. Infected devils were more active within their network late in the mating season, a pattern with repercussions for DFTD transmission. Our study provides a rare opportunity to quantify and understand the behavioural feedbacks of disease in wildlife and how they may affect transmission and population dynamics in general.</p>
Interaction network for eleven Cucurbita pepo sites in Guatemala
<p><span>In recent years, evidence has been found that plant-pollinator interactions are altered by land-use and that genetic diversity also plays a role. However, how land-use and genetic diversity influence plant-pollinator interactions, particularly in the Neotropics, where many endemic plants exist is still an open question. <i>Cucurbita pepo</i> is a monoecious plant and traditional crop wide distributed, with high rates of molecular evolution, landraces associated with human cultural management and a history of coevolution with bees, which makes this species a promising model for studying the effect of landscape and genetic diversity on plant-pollinator interactions. Here, we assess 1) whether female and male flowers differences have an effect on the interaction network, 2) how C<i>. pepo</i> genetic diversity affects flower-bee visitation network structure, and 3) what is the effect that land-use, accounting for <i>C. pepo</i> genetic variability<i>, </i>has on pumpkin-bee interaction network variables. Our results indicate that female and male flowers presented the same community composition and network structure suggesting that the differences do not have a significant effect on network evolution. Genetic diversity has a positive effect on modularity, nestedness, and number of interactions, which, when considered, allows to observe how land-use variables can have an enhancing or buffering effect on nestedness. Our results suggest that considering genetic diversity is relevant for a better understanding of the effect of landscape on interaction networks. Additionally, this understanding has great value in conserving biodiversity and enhancing the stability of interaction networks in a world facing great challenges of habitat and diversity loss.</span></p>
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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.