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21 results for “co-occurrence network”
Data for the paper 'Reducing networks of ethnographic codes co-occurrence in anthropology'
<p>Pseudonymized data supporting the paper "Reducing networks of ethnographic codes co-occurrence in anthropology", published in "Advances in Quantitative Ethnography. Fourth International Conference on Quantitative Ethnography (ICQE 2022), Copenhagen, Denmark, October 15–19, 2022, Proceedings", and edited by Amanda Barany and Crina Damsa. The paper is part of the POPREBEL project. The data were gathered in the spring and summer of 2021, as a part of a larger research project on populism in Central and Eastern Europe, to be completed by the end of 2022. They consist of 17 semi-structured interviews with Polish-speaking Internet users, who used social media to seek and share information about health against the backdrop of the COVID-19 pandemic. Research participants were asked about their opinion on the current state of affairs in their respective countries, and their political choices over the years and at present.</p> <p><a href="https://edgeryders.eu/t/long-term-ssna-data-storage-documentation-manual/12786">Data export and documentation process</a> (contains links to the code used to export the data).</p>
Implementing social network analysis to understand the socio-ecology of wildlife co-occurrence and joint interactions with humans in anthropogenic environments
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Inferring microbial co-occurrence network from amplicon data: a systematic evaluation
<p>Supporting data for the manuscript "<em>Inferring microbial co-occurrence network from amplicon data: a systematic evaluation</em>".</p>
Data from: Integrating variation in bacterial-fungal co-occurrence network with soil carbon dynamics
<p>Bacteria and fungi are core microorganisms in diverse ecosystems, and their cross-kingdom interactions are considered key determinants of microbiome structure and ecosystem functioning. However, how bacterial-fungal interactions mediate soil organic carbon (SOC) dynamics remains largely unexplored in the context of artificial forest ecosystems. Here, we characterized soil bacterial and fungal communities in four successive planting of Eucalyptus and compared them to a neighboring evergreen broadleaf forest. Carbon (C) mineralization combined with five C-degrading enzymatic activities was investigated to determine the effects of successive planting of Eucalyptus on SOC dynamics. Our results indicated that successive planting of Eucalyptus significantly altered the diversity and structure of soil bacterial and fungal communities and increased the negative bacterial-fungal associations. The bacterial diversity significantly decreased in all Eucalyptus plantations compared to the evergreen forest, while the fungal diversity showed the opposite trend. The ratio of negative bacterial-fungal associations increased with successive planting of Eucalyptus due to the decrease in SOC, ammonia nitrogen (NH4+−N), nitrate nitrogen (NO3−−N), and available phosphorus (AP). Structural equation modeling indicated that the potential cross-kingdom competition, based on the ratio of negative bacterial-fungal correlations, was significantly negatively associated with the diversity of total bacteria and keystone bacteria, thereby increasing C-degrading enzymatic activities and C mineralization.</p> <p>Synthesis and applications: Our results highlight the regulatory role of the negative bacterial-fungal association in enhancing the correlation between bacterial diversity and C mineralization. This suggests that promoting short-term successive planting in the management of Eucalyptus plantations can mitigate the impact of this association on SOC decomposition. Taken together, our study advances the understanding of bacterial-fungal negative associations to mediate carbon mineralization in Eucalyptus plantations, giving us a new insight into SOC cycling dynamics in artificial forests.</p>
Data from: Road disturbance shifts root fungal symbiont types and reduces the connectivity of plant-fungal co-occurrence networks in mountains
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Data from: Environmental and spatial effects on co-occurrence network size and taxonomic similarity in stream diatoms, insects, and fish
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Data from: Integrating variation in bacterial-fungal co-occurrence network with soil carbon dynamics
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Data from: Plant-mycorrhizal fungus co-occurrence network lacks substantial structure
The interactions between plants and arbuscular mycorrhizal fungi (AMF) maintain a crucial link between macroscopic organisms and the soil microbial world. These interactions are of extreme importance for the diversity of plant communities and ecosystem functioning. Despite this importance, only recently has the structure of plant–AMF interaction networks been studied. These recent studies, which used genetic data, suggest that these networks are highly structured, very similar to plant–animal mutualistic networks. However, the assembly process of plant–AMF communities is still largely unknown, and an important feature of plant–AMF interactions has not been incorporated: they occur at an extremely localized scale. Studying plant–AMF networks in a spatial context seems therefore a crucial step. This paper studies a plant–AMF spatial co-occurrence network using novel methodology based on information theory and a unique set of spatially explicit species-level data. We apply three null models of which only one accounts for spatial effects. We find that the data show substantial departures from null expectations for the two non-spatial null models. However, for the null model considering spatial effects, there are few significant co-occurrences compared with the other two null models. Thus, plant–AMF spatial co-occurrences seem to be mostly explained by stochasticity, with a small role for other factors related to plant–AMF specialization. Furthermore, we find that the network is not significantly nested or modular. We conclude that this plant–AMF spatial co-occurrence network lacks substantial structure and, therefore, plants and AMF species do not track each other over space. Thus, random encounters seem more important in the first step of the assembly of plant–AMF communities.
Data from: Dynamic species co-occurrence networks require dynamic biodiversity surrogates
In conservation it is inevitable that surrogates be selected to represent the occurrence of hard–to–find species and find priority locations for management. However, species co–occurrence can vary over time. Here we demonstrate how temporal dynamics in species co–occurrence influence the ability of managers to choose the best surrogate species. We develop an efficient optimisation formulation that selects the optimal set of complementary surrogate species from any co–occurrence network. We apply it to two Australian datasets on successional bird responses to disturbances of revegetation and fire. We discover that a surprisingly small number of species are required to represent the majority of species co–occurrences at any one time. Because co–occurrence patterns are temporally dynamic, the optimal set of surrogates, and the number of surrogates required to achieve a desired surrogacy power, depend on sampling effort and the successional state of a system. Overlap in optimal sets of surrogates for representing 70% of co–occurring species ranges from zero to 57% depending on when the surrogacy decision is made. Surrogate sets representing early successional communities over–estimate the power of surrogacy decisions at later times. Our results show that in dynamic systems, optimal surrogates might be selected in different ways: (1) use short–term monitoring to choose a larger number of static less–informative surrogates; (2) use long–term monitoring to choose a smaller number of static high–power surrogates that may poorly represent early successional co–occurrence; (3) develop adaptive surrogate selection frameworks with high short–term and long–term surrogacy power that update surrogate sets and capture temporal dynamics in species co–occurrence. Our results suggest vigilance is needed when selecting surrogates for other co–occurring species in dynamic landscapes, as selected surrogates from one time may have reduced effectiveness at a different time. Ultimately, decisions that fail to acknowledge dynamic species co–occurrence will lead to uninformative or redundant surrogates.
FoS Co-occurrence networks related to 103 FoS, and Direct ancestors to emerging topics between 2001~2010
<p>FoS Co-occurrence networks related to 311 Level 0 and 1 FoS, and Direct ancestors to emerging topics between 2000~2010.</p> <p> </p> <p>graphs.7z contains python graph binary files, and Golden dataset.7z contains python list binary files.</p>
Data from: Seeing is believing? comparing plant-herbivore networks constructed by field co-occurrence and DNA barcoding methods for gaining insights into network structures
Plant-herbivore interaction networks provide information about community organization. Two methods are currently used to document pairwise interactions among plants and insect herbivores. One is the traditional method that collects plant-herbivore interaction data by field observation of insect occurrence on host plants. The other is the increasing application of newly developed molecular techniques based on DNA barcodes to the analysis of gut contents. The second method is more appealing because it documents realized interactions. To construct complete networks, each technique of network construction is urgent to be assessed. We addressed this question by comparing the effectiveness and reliability of the two methods in constructing plant-Lepidoptera larval network in a 50 ha subtropical forest in China. Our results showed that the accuracy of diet identification by observation method increased with the number of observed insect occurrences on food plants. In contrast, the molecular method using three plant DNA markers were able to identify food residues for 35.6% larvae and correctly resolved 77.3% plant (diet) species. Network analysis showed molecular networks had three-fold more unique host plant species but fewer links than the traditional networks had. The molecular method detected plants that were not sampled by the traditional method, e.g., bamboos, bryophytes and lianas in the diets of insect herbivores. The two networks also possessed significantly different structural properties. Our study indicates the traditional observation of co-occurrence is inadequate, while molecular method can provide higher species resolution of ecological interactions.
Data from: Species co-occurrence networks show reptile community reorganization under agricultural transformation
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Data from: Dynamic species co-occurrence networks require dynamic biodiversity surrogates
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Data from: Seeing is believing? comparing plant-herbivore networks constructed by field co-occurrence and DNA barcoding methods for gaining insights into network structures
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Data from: Plant-mycorrhizal fungus co-occurrence network lacks substantial structure
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Diurnal foraging ant–tree co-occurrence networks are similar between canopy and understorey in a Neotropical rain forest
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The impacts of nutrient supply and imbalance on subcontinental co-occurrence networks and metacommunity composition of stream algae
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Data from: Taxon abundance, diversity, co-occurrence and network analysis of the ruminal microbiota in response to dietary changes in dairy cows
The effects of sunflower oil (SO) (0 or 50 g/kg diet dry matter), supplemented to diets contrasting in the proportion of forage and concentrate (FC) (65:35 vs 35:65), were evaluated for their influence on rumen microbiome. Four multiparous Nordic Red dairy cows fitted with rumen cannulae were used in a 4 × 4 Latin square with a 2 × 2 factorial arrangement of treatments and four 35-d periods. Ruminal digesta samples were collected on d 22 and d 24 of each experimental period and DNA was extracted from a combined sample. Diet effect on rumen microbial community was explored by qPCR, T-RFLP and metabarcoding sequencing. QPCR analysis showed that the total amounts of bacteria, archaea or ciliate protozoa were not significantly altered either by FC ratio or addition of SO. Only fungi were reduced by half in high concentrate (H) compared to high forage (L) diets (P=0.03). Further significant reduction of fungi was observed due to SO in both HSO and LSO diets but the effect was stronger in HSO (H vs HSO by 10.5x, P=0.03; L vs LSO by 1.9x, P=0.04). Metabarcoding sequencing analysis showed that SO affected bacterial, archaeal, ciliate protozoa and fungal community structure and diversity and the effect was FC ratio dependent. As expected, Simpson's index of diversity was higher in diets containing higher proportion of forage. These diets were dominated by Firmicutes, while Bacteroidetes and Proteobacteria were more abundant in H diets. Methanobrevibacter ruminantium and Methanobrevibacter gottschalkii dominated archaea community but they were in negative relation to each other. Methanobrevibacter gottschalkii was more abundant in L, while Methanobrevibacter ruminantium in H diet. The strongest diet effect was observed on fungal community, represented by both well classified and novel fungal groups. Both, increase in concentrate and supplementation of SO significantly reduced fungal diversity. We explored microbial interactions by building taxa co-occurrence networks. Our results suggest that studying entire rumen microbiome simultaneously is needed aiming to better understand how diet induced changes within microbial community are associated with microbial function, subsequently leading to better understanding of rumen fermentation and methanogenesis.
Data from: An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks
1. Systematic review, meta-analysis, and other forms of evidence synthesis are critical to strengthen the evidence base concerning conservation issues and to answer ecological and evolutionary questions. Synthesis lags behind the pace of scientific publishing, however, due to time and resource costs which partial automation of evidence synthesis tasks could reduce. Additionally, current methods of retrieving evidence for synthesis are susceptible to bias towards studies with which researchers are familiar. In fields that lack standardized terminology encoded in an ontology, including ecology and evolution, research teams can unintentionally exclude articles from the review by omitting synonymous phrases in their search terms. 2. To combat these problems, we developed a quick, objective, reproducible method for generating search strategies that uses text mining and keyword co-occurrence networks to identify the most important terms for a review. The method reduces bias in search strategy development because it does not rely on a predetermined set of articles and can improve search recall by identifying synonymous terms that research teams might otherwise omit. 3. When tested against the search strategies used in published environmental systematic reviews, our method performs as well as the published searches and retrieves gold-standard hits that replicated versions of the original searches do not. Because the method is quasi-automated, the amount of time required to develop a search strategy, conduct searches, and assemble results is reduced from approximately 17-34 hours to under 2 hours. 4. To facilitate use of the method for environmental evidence synthesis, we implemented the method in the R package litsearchr, which also contains a suite of functions to improve efficiency of systematic reviews by automatically deduplicating and assembling results from separate databases.
Data from: An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks
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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.