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1,721 results for “network data”
Data from: Genetic regulatory network motifs constrain adaptation through curvature in the landscape of mutational (co)variance
Systems biology is accumulating a wealth of understanding about the structure of genetic regulatory networks, leading to a more complete picture of the complex genotype-phenotype relationship. However, models of multivariate phenotypic evolution based on quantitative genetics have largely not incorporated a network-based view of genetic variation. Here we model a set of two-node, two-phenotype genetic network motifs, covering a full range of regulatory interactions. We find that network interactions result in different patterns of mutational (co)variance at the phenotypic level (the M-matrix), not only across network motifs but also across phenotypic space within single motifs. This effect is due almost entirely to mutational input of additive genetic (co)variance. Variation in M has the effect of stretching and bending phenotypic space with respect to evolvability, analogous to the curvature of space-time under general relativity, and similar mathematical tools may apply in each case. We explored the consequences of curvature in mutational variation by simulating adaptation under divergent selection with gene flow. Both standing genetic variation (the G-matrix) and rate of adaptation are constrained by M, so that G and adaptive trajectories are curved across phenotypic space. Under weak selection the phenotypic mean at migration-selection balance also depends on M.
Data from: Spatially structured statistical network models for landscape genetics
A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape-genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non-binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape-genetic data. We further explore a set of spatial-statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape-genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape-genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.
Data from: Implications of non-native species for mutualistic network resistance and resilience
Resilience theory aims to understand and predict ecosystem state changes resulting from disturbances. Non-native species are ubiquitous in ecological communities and integrated into many described ecological interaction networks, including mutualisms. By altering the fitness landscape and rewiring species interactions, such network invasion may carry important implications for ecosystem resistance and resilience under continued environmental change. Here, I hypothesize that the tendency of established non-native species to be generalists may make them more likely than natives to occupy central network roles and may link them to the resistance and resilience of the overall network. I use a quantitative research synthesis of 58 empirical pollination and seed dispersal networks, along with extinction simulations, to examine the roles of known non-natives in networks. I show that non-native species in networks enhance network redundancy and may thereby bolster the ecological resistance or functional persistence of ecosystems in the face of disturbance. At the same time, non-natives are unlikely to partner with specialist natives, thus failing to support the resilience of native species assemblages. Non-natives significantly exceed natives in network centrality, normalized degree, and Pollination Service Index. Networks containing non-natives exhibit lower connectance, more links on average, and higher generality and vulnerability than networks lacking non-natives. As environmental change progresses, specialists are particularly likely to be impacted, reducing species diversity in many communities and network types. This work implies that functional diversity may be retained but taxonomic diversity decline as non-native species become established in networks worldwide.
Data from: Developmental stress predicts social network position
The quantity and quality of social relationships, as captured by social network analysis, can have major fitness consequences. Various studies have shown that individual differences in social behaviour can be due to variation in exposure to developmental stress. However, whether these developmental differences translate to consistent differences in social network position is not known. We experimentally increased levels of the avian stress hormone corticosterone (CORT) in nestling zebra finches in a fully balanced design. Upon reaching nutritional independence, we released chicks and their families into two free-flying rooms, where we measured daily social networks over five weeks using passive integrated transponder tags. Developmental stress had a significant effect on social behaviour: despite having similar foraging patterns, CORT chicks had weaker associations to their parents than control chicks. Instead, CORT chicks foraged with a greater number of flock mates and were less choosy with whom they foraged, resulting in more central network positions. These findings highlight the importance of taking developmental history into account to understand the drivers of social organization in gregarious species.
Data from: The geographical variation of network structure is scale dependent: understanding the biotic specialization of host-parasitoid networks
Research on the structure of ecological networks suggests that a number of universal patterns exist. Historically, biotic specialization has been thought to increase towards the Equator. Yet, recent studies have challenged this view showing non-conclusive results. Most studies analysing the geographical variation in biotic specialization focus, however, only on the local scale. Little is known about how the geographical variation of network structure depends on the spatial scale of observation (i.e., from local to regional spatial scales). This should be remedied, as network structure changes as the spatial scale of observation changes, and the magnitude and shape of these changes can elucidate the mechanisms behind the geographical variation in biotic specialization. Here we analyse four facets of biotic specialization in host-parasitoid networks along gradients of climatic constancy, classifying the networks according to their spatial extension (local or regional). Namely, we analyse network connectance, consumer diet overlap, consumer diet breadth, and resource vulnerability at both local and regional scales along the gradients of both current climatic constancy and historical climatic change. While at the regional scale none of the climatic variables are associated to biotic specialization, at the local scale, network connectance, consumer diet overlap, and resource vulnerability decrease with current climatic constancy, whereas consumer generalism increases (i.e., broader diet breadths in tropical areas). Similar patterns are observed along the gradient of historical climatic change. We provide an explanation based on different beta-diversity for consumers and resources across the geographical gradients. Our results show that the geographical gradient of biotic specialization is not universal. It depends on both the facet of biotic specialization and the spatial scale of observation.
Data from: Patent foramen ovale closure, antiplatelet therapy or anticoagulation in patients with patent foramen ovale and cryptogenic stroke: a systematic review and network meta-analysis incorporating complementary external evidence
Objective: To examine the relative impact of three management options in patients less than 60 years old with cryptogenic stroke and a patent foramen ovale (PFO): PFO closure plus antiplatelet therapy, antiplatelet therapy alone, and anticoagulation alone. Design: Systematic review and network meta-analysis (NMA) supported by complementary external evidence Data sources: Medline, EMBASE, and Cochrane CENTRAL. Study selection: Randomised controlled trials (RCTs) addressing PFO closure and/or medical therapies in patients with PFO and cryptogenic stroke. Review methods: We conducted an NMA complemented with external evidence and rated certainty of evidence using the GRADE system. Results: Ten RCTs in eight studies proved eligible (n=4416). PFO closure versus antiplatelet therapy probably results in substantial reduction in ischaemic stroke recurrence (risk difference per 1000 patients over 5 years [RD]: -87, 95% credible interval [CrI] -100 to -33; moderate certainty). Compared with anticoagulation, PFO closure may confer little or no difference in ischaemic stroke recurrence (low certainty) but probably has a lower risk of major bleeding (RD -20, 95% Crl -27 to -2, moderate certainty). Relative to either medical therapy, PFO closure probably increases the risk of persistent atrial fibrillation (RD 18, CI +5 to +56, moderate certainty) and device-related adverse events (RD +36, 95% CI +23 to +50, high certainty). Anticoagulation, compared to antiplatelet therapy, may reduce the risk of ischaemic stroke recurrence (RD -71, 95% CrI -100 to +17, low certainty), but probably increases the risk of major bleeding (RD +12, CrI -5 to +65, moderate certainty). Conclusions: In patients less than 60 years old, PFO closure probably confers an important reduction in ischaemic stroke recurrence compared to antiplatelet therapy alone but may make no difference compared to anticoagulation. PFO closure incurs a risk of persistent atrial fibrillation and device-related adverse events. Compared to alternatives, anticoagulation probably increases major bleeding.
Data from: Can longitudinal generalized estimating equation models distinguish network influence and homophily? an agent-based modeling approach to measurement characteristics
Background: Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. Methods: We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. Results: In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. Conclusions: The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.
Data from: Population genomic analysis suggests strong influence of river network on spatial distribution of genetic variation in invasive saltcedar across the southwestern US
Understanding the complex influences of landscape and anthropogenic elements that shape the population genetic structure of invasive species provides insight into patterns of colonization and spread. The application of landscape genomics techniques to these questions may offer detailed, previously undocumented insights into factors influencing species invasions. We investigated the spatial pattern of genetic variation and the influences of landscape factors on population similarity in the invasive riparian shrub saltcedar (Tamarix L.) by analyzing 1,997 genome-wide SNP markers for 259 individuals from 25 populations collected throughout the southwestern US. Our results revealed a broad-scale spatial genetic differentiation of saltcedar populations between the Colorado and Rio Grande river basins and identified potential barriers to population similarity along both river systems. River pathways most strongly contributed to population similarity. In contrast, low temperature and dams likely served as barriers to population similarity. We hypothesize that large-scale geographic patterns in genetic diversity resulted from a combination of early introductions from distinct populations, the subsequent influence of natural selection, dispersal barriers, and founder effects during range expansion.
Data from: Resilience or robustness: identifying topological vulnerabilities in rail networks
Many critical infrastructure systems have network structure and are under stress. Despite their national importance, the complexity of large-scale transport networks means we do not fully understand their vulnerabilities to cascade failures. The research in this paper examines the interdependent rail networks in Greater London and surrounding commuter area. We focus on the morning commuter hours, where the system is under the most demand stress. There is increasing evidence that the topological shape of the network plays an important role in dynamic cascades. Here, we examine whether the different topological measures of resilience (stability) or robustness (failure) are more appropriate for understanding poor railway performance. The results show that resilience and not robustness has a strong correlation to the consumer experience statistics. Our results are a way of describing the complexity of cascade dynamics on networks without the involvement of detailed agent-based-models, showing that cascade effects are more responsible for poor performance than failures. The network science analysis hints at pathways towards making the network structure more resilient by reducing feedback loops.
Data from: FlatNJ: a novel network-based approach to visualize evolutionary and biogeographical relationships
Split networks are a type of phylogenetic network that allow visualization of conflict in evolutionary data. We present a new method for constructing such networks called FlatNetJoining (FlatNJ). A key feature of FlatNJ is that it produces networks that can be drawn in the plane in which labels may appear inside of the network. For complex data sets that involve, for example, non-neutral molecular markers, this can allow additional detail to be visualized as compared to previous methods such as split decomposition and NeighborNet. We illustrate the application of FlatNJ by applying it to whole HIV genome sequences, where recombination has taken place, fluorescent proteins in corals, where ancestral sequences are present, and mitochondrial DNA sequences from gall wasps, where biogeographical relationships are of interest. We find that the networks generated by FlatNJ can facilitate the study of genetic variation in the underlying molecular sequence data and, in particular, may help to investigate processes such as intra-locus recombination. FlatNJ has been implemented in Java and is freely available at www.uea.ac.uk/computing/software/flatnj.
Data from: Dynamic antagonism between phytochromes and PIF-family bHLHs induces selective reciprocal responses to light and shade in a rapidly responsive transcriptional network in Arabidopsis
Plants respond to shade-modulated light-signals, via the phytochrome (phy) system, by adaptive changes, collectively termed the shade avoidance syndrome (SAS). To examine the roles of the Phy-Interacting bHLH Factors, PIF1, 3, 4 and 5, in relaying this information to the transcriptional network, we compared the genome-wide expression profiles of wild-type and quadruple pif (pifq) mutants in response to shade. The data identify a subset of genes, enriched in transcription-factor-encoding loci, that respond rapidly (within 1 h), in a PIF-dependent manner, to the shade signal, and that contain promoter-located G-box-sequence motifs (CACGTG), known to be preferred PIF binding sites. These genes are thus potential direct targets of phy-PIF signaling that function in the primary transcriptional circuitry controlling downstream response-elaboration. A second subset of PIF-dependent, early-response genes, lacking G-box motifs, are enriched for auxin-responsive loci, suggestive of being indirect targets of phy-PIF signaling involved in the rapid cell-expansion known to be induced by shade. A meta-analysis comparing deetiolation- and shade-responsive transcriptomes identifies a further subset of G-box-containing genes that reciprocally display rapid repression and induction in response to light and shade signals at the inception of deetiolation and shade-avoidance, respectively. Collectively, these data define a core set of transcriptional and hormonal (auxin, cytokinin) processes that appear to be dynamically poised to react rapidly to changes in the light environment via perturbations in the mutually antagonistic actions of the phys and PIFs. Data from comparative analysis of the quadruple pifq and all triple pif-mutant combinations in response to light and shade, confirm that the PIF-quartet members act with overlapping redundancy on seedling morphogenesis and transcriptional regulation, but that the individual PIFs contribute differentially to these responses.
Data from: Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure
Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyse such data lag behind the ability to generate it, particularly in nonmodel species. Linkage disequilibrium (LD, the nonrandom association of alleles from different loci) is a highly sensitive indicator of many evolutionary phenomena including chromosomal inversions, local adaptation and geographical structure. Here, we present linkage disequilibrium network analysis (LDna), which accesses information on LD shared between multiple loci genomewide. In LD networks, vertices represent loci, and connections between vertices represent the LD between them. We analysed such networks in two test cases: a new restriction-site-associated DNA sequence (RAD-seq) data set for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterized single nucleotide polymorphism (SNP) data set from 21 three-spined stickleback individuals. In each case, we readily identified five distinct LD network clusters (single-outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, further population-genetic analyses supported the inference that each SOC corresponds to a large inversion, consistent with previous cytological studies. For sticklebacks, we inferred that each SOC was associated with a distinct evolutionary phenomenon: two chromosomal inversions, local adaptation, population-demographic history and geographic structure. LDna is thus a useful exploratory tool, able to give a global overview of LD associated with diverse evolutionary phenomena and identify loci potentially involved. LDna does not require a linkage map or reference genome, so it is applicable to any population-genomic data set, making it especially valuable for nonmodel species.
Data from: The relative efficiency of modular and non-modular networks of different size
Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: 'small' and 'large', and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.
Data from: Phylogenetic tree shape and the structure of mutualistic networks
Species community composition is known to alter the network of interactions between two trophic levels, potentially affecting its functioning (e.g. plant pollination success) and the stability of communities. Phylogenies vary in shape with regard to the rate of evolutionary change across a tree (influencing tree balance) and variation in the timing of branching events (affecting the distribution of node ages in trees), both of which may influence the structure of species interaction networks. Because related species are likely to share many of the traits that regulate interactions, the shape of phylogenetic trees may provide some insights into the distribution of traits within communities, and hence the likelihood of interaction among species. However, little attention has been paid to the potential effects of changes in phylogenetic diversity (PD) on interaction networks. Phylogenetic diversity is influenced by species diversity within a community, but also how distantly-related the constituent species are from one another. Here, we evaluate the relationship between two important measures of phylogenetic diversity (tree shape and age of nodes) and the structure of plant-pollinator interaction networks using empirical and simulated data. Whereas the former allows us to evaluate patterns in real communities, the latter allows us to evaluate more systematically the relationship between tree shape and network structure under three different models of trait evolution. In empirical networks, less balanced plant phylogenies were associated with lower connectance in interaction networks indicating that communities with the descendants of recent radiations are more diverged and specialized in their partnerships. In simulations, tree balance and the distribution of nodes through time were included in the best models for modularity, and the second best models for connectance and nestedness. In models assuming random evolutionary change through time (i.e., Brownian motion), less balanced trees and trees with nodes near the tips exhibited greater modularity, whereas in models with an early burst of radiation followed by relative stasis (i.e. early-burst models) more balanced trees and trees with nodes near roots had greater modularity. Synthesis: Overall, these results suggest that the shape of phylogenies can influence the structure of plant-pollinator interaction networks. However, the mismatch between simulations and empirical data indicate that no simple model of trait evolution mimics that observed in real communities.
Data from: Notch and Nodal control expression of a forkhead factor in the specification network of a multipotent progenitor population in sea urchin
Indirect development, where embryogenesis gives rise to a larval form, requires that some cells retain developmental potency until they contribute to the different tissues in the adult, including the germ line, in a later, post-embryonic phase. In sea urchins, the coelomic pouches are the major contributor to the adult but how coelomic pouch cells (CPCs) are specified during embryogenesis is unknown. We here identify the key signaling inputs into the CPC specification network and show the forkhead factor foxY is the first transcription factor specifically expressed in CPC progenitors. Through dissection of its cis-regulatory apparatus we determine that the foxY expression pattern is the result of two signaling inputs: First, Delta/Notch signaling activates foxY in CPC progenitors and, second, Nodal signaling restricts its expression to the left side, where the adult rudiment will form, through direct repression by the Nodal target pitx2. A third signal, Hedgehog, is required for coelomic pouch morphogenesis and institution of laterality but does not directly affect foxY transcription. Knockdown of foxY results in failure to form coelomic pouches and disrupts expression of virtually all transcription factors known to be expressed in this cell type. Our experiments place foxY at the top of the regulatory hierarchy underlying specification of a cell type maintaining developmental potency.
Data from: Managing seagrass resilience under cumulative dredging affecting light: predicting risk using dynamic Bayesian networks
Coastal development is contributing to ongoing declines of ecosystems globally. Consequently, understanding the risks posed to these systems, and how they respond to successive disturbances, is paramount for their improved management. We study the cumulative impacts of maintenance dredging on seagrass ecosystems as a canonical example. Maintenance dredging causes disturbances lasting weeks to months, often repeated at yearly intervals. We present a risk-based modelling framework for time varying complex systems centred around a dynamic Bayesian network (DBN). Our approach estimates the impact of a hazard on a system's response in terms of resistance, recovery and persistence, commonly used to characterise the resilience of a system. We consider whole-of-system interactions including light reduction due to dredging (the hazard), the duration, frequency and start time of dredging, and ecosystem characteristics such as the life-history traits expressed by genera and local environmental conditions. The impact on resilience of dredging disturbances is evaluated using a validated seagrass ecosystem DBN for meadows of the genera Amphibolis (Jurien Bay, WA, Australia), Halophila (Hay Point, Qld, Australia) and Zostera (Gladstone, Qld, Australia). Although impacts varied by combinations of dredging parameters and the seagrass meadows being studied, in general, 3 months of duration or more, or repeat dredging every 3 or more years, were key thresholds beyond which resilience can be compromised. Additionally, managing light reduction to less than 50% can significantly decrease one or more of loss, recovery time and risk of local extinction, especially in the presence of cumulative stressors. Synthesis and applications. Our risk-based approach enables managers to develop thresholds by predicting the impact of different configurations of anthropogenic disturbances being managed. Many real-world maintenance dredging requirements fall within these parameters, and our results show that such dredging can be successfully managed to maintain healthy seagrass meadows in the absence of other disturbances. We evaluated opportunities for risk mitigation using time windows; periods during which the impact of dredging stress did not impair resilience.
Data from: Coevolution and the architecture of mutualistic networks
Although coevolution is widely recognized as an important evolutionary process for pairs of reciprocally specialized species, its importance within species-rich communities of generalized species has been questioned. Here we develop and analyze mathematical models of mutualistic communities, such as those between plants and pollinators or plants and seed-dispersers to evaluate the importance of coevolutionary selection within complex communities. Our analyses reveal that coevolutionary selection can drive significant changes in trait distributions with important consequences for the network structure of mutualistic communities. One such consequence is greater connectance caused by an almost invariable increase in the rate of mutualistic interaction within the community. Another important consequence is altered patterns of nestedness. Specifically, interactions mediated by a mechanism of phenotype matching tend to be anti-nested when coevolutionary selection is weak and even more strongly anti-nested as increasing coevolutionary selection favors the emergence of reciprocal specialization. In contrast, interactions mediated by a mechanism of phenotype differences tend to be nested when coevolutionary selection is weak, but less nested as increasing coevolutionary selection favors greater levels of generalization in both plants and animals. Taken together, our results show that coevolutionary selection can be an important force within mutualistic communities, driving changes in trait distributions, interaction rates, and even network structure.
Data from: Refining the trophic diversity, ecological network structure, and bottom-up importance of prey groups for temperate reef fishes
<p>The file "Zarco-Perello et al Temperate Reef Fish Trophic Guilds Complete Diet Dataset.xlsx" contains several spreadsheet tabs related to the analyses carried out in the paper: <i><strong>Refining the trophic diversity, ecological network structure, and bottom-up importance of prey groups for temperate reef fishes: </strong></i><a href="https://doi.org/10.32942/X2CC97">https://doi.org/10.32942/X2CC97</a></p><p>All analyses, with the exception of the network calculations, of the study were carried out in the computer software R. The code is contained in the file "Zarco-Perello et al Temperate Reef Fish Trophic Ecology.R". For trophic network analyses we used the computer program Gephi v0.1 <a href="https://sciwheel.com/work/citation?ids=15257446&amp;pre=&amp;suf=&amp;sa=0">(Bastian et al. 2009).</a></p><p><strong>DATASET DESCRIPTION</strong></p><p><strong>Region of Study</strong></p><p>The region of study encompasses all the temperate reefs of south-western Australia (SWA). Extending along ~1600 km of coast, from Jurien Bay Marine Park (30° 18.6 S, 115° 0.1 E) to the Recherche Archipelago Nature Research (33° 53.7 S, 123° 52.3 E; supplementary Fig. S1), the temperate reefs of SWA are distributed across the Leeuwin and Houtman biogeographical ecoregions <a href="https://sciwheel.com/work/citation?ids=1796477&pre=&suf=&sa=0">(Spalding et al. 2007)</a>, conforming approximately ⅓ of the total distribution of temperate Australia, known as the Great Southern Reef <a href="https://sciwheel.com/work/citation?ids=4498783&pre=&suf=&sa=0">(Bennett et al. 2016).</a></p><p><strong>Species Composition</strong></p><p>The species composition of the metacommunity of temperate reef fishes of the region was obtained from a total of 4589 underwater visual surveys conducted across 206 reefs in 12 locations by the Reef Life Survey (RLS) citizen science program, and the Australian Temperate Reef Collaboration (ATRC, with support from the Department of Biodiversity Conservation and Attractions; https://www.atrc.au) from 1997 to 2021.</p><p><strong>Trophic Information</strong></p><p>All fish species listed in the RLS-ATRC database were classified in trophic guilds based on collected diet information from studies of gut content analyses in SWA, or other Australian and international regions in the absence of local information. A total of 298 fish species composed the metacommunity. For every species, we obtained diet information from the scientific literature reported on Fishbase <a href="https://sciwheel.com/work/citation?ids=10423542&pre=&suf=&sa=0">(Froese and Pauly 2019)</a> and through the search engine Scopus using the search terms: TS = (<i>name of species</i>* OR *<i>common name of species</i>*) AND TS = (diet OR *stomach content* OR *gut content* OR consump* OR herbi* OR predat* OR feeding). Diet information consisted of the average proportions of food items represented as the number of items (%N), percent volume (%V), or biomass (%W) in a population of each species. Preference was given to diet studies conducted in the region of study and those presenting biomass proportions. Species that lacked diet information globally were assigned diet proportions based on phylogenetically related species with similar size and habitat preferences based on the Fish Tree of Life <a href="https://sciwheel.com/work/citation?ids=10720381&pre=&suf=&sa=0&dbf=0">(Chang et al. 2019)</a>.</p><p><i><< The tab "Guilds Complete Diet Dataset" contains all the diet information (stomach content proportions) and its sources for all fish species considered in the study >></i></p><p><strong>Trophic guilds classification</strong></p><p>To quantify the diversity of trophic guilds and identify important fish consumers of specific groups of prey, we classified the fish species into trophic guilds performing a multi-step cluster analysis. Firstly, species were grouped into main trophic guilds using the mutually exclusive major categories of prey items. The diet proportions in these categories were used to create a dissimilarity matrix among species based on the Bray-Curtis linkage method using the function <i>vegdist</i> of the R package Vegan <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>, which was used to run a sequential divisive hierarchical cluster analysis using the function <i>diana</i> (divisive analysis) of the R package Cluster <a href="https://sciwheel.com/work/citation?ids=15165291&pre=&suf=&sa=0">(Maechler et al. 2022)</a>. Subsequently, because there are mismatches in the resolution of diet identification between species belonging to different trophic levels (<i>e.g.</i> the diets of herbivorous fish tend to have higher resolution on macrophytes, while carnivorous species tend to have higher resolution on animal prey), species within each identified main trophic guild were subject to a cluster analysis with higher definition of prey items to identify groups of species with diet specializations using sequential agglomerative hierarchical cluster analysis based on Ward's Method and Bray-Curtis or Euclidean dissimilarity matrix <a href="https://sciwheel.com/work/citation?ids=205080&pre=&suf=&sa=0">(Pineda‑Munoz and Alroy 2014)</a>.</p><p>The stomach content of most scarid species (parrotfish; Labridae: Scarinae) is very difficult to identify due to their pharyngeal mill, which grinds all food items to indiscernible particles. However, they are well identified as a special group that ingest detritus and algae by scraping the reef substrate with their specialized fused teeth. Thus, for the sake of differentiating their trophic guild, the proportions of diet for species of parrotfish was arbitrarily defined based on field observations as sediment and detritus (90%) and short filamentous algae (10%) <a href="https://sciwheel.com/work/citation?ids=11332249&pre=&suf=&sa=0&dbf=0">(Bonaldo et al. 2014)</a>. Additionally, cleaner fish and false cleaners are a special group of fishes that are difficult to group by diet given that they feed on prey that could be identified as zooplankton or zoobenthos, while in fact true cleaners forage, at least in part, on parasitic invertebrates attached to bigger fish, in addition to fish skin and scales <a href="https://sciwheel.com/work/citation?ids=13921938&pre=&suf=&sa=0">(Grutter 1997)</a>; thus, given their particular trophic ecology these labrid and blenny species were arbitrarily grouped in the major trophic group "fish cleaners" for the subsequent specialized trophic group classifications.</p><p>Visual analysis of the differences in multidimensional space between trophic guilds was done with Non-metric Multidimensional Scaling based on the dissimilarity matrix calculated for clustering using the function <i>metaMDS</i> of the R package vegan (reported in supplementary materials; <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>. Statistical significance in dietary differences among major and specialized trophic guilds (diet proportions ~ trophic guilds) was tested with permutational analysis of variance (PERMANOVA) using the function <i>adonis2 </i>of the R package vegan <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>, followed by pairwise comparisons using the function <i>pairwise.adonis2</i> of the R package pairwiseAdonis <a href="https://sciwheel.com/work/citation?ids=15190336&pre=&suf=&sa=0">(Martinez 2017)</a>.</p><p><i><< The tabs in the dataset called "Major Guilds Diet Data", "Herbivores Diet Data", "Cleaners Diet Data", "Zoobenthivores Diet Data, "Zooplanktivores Diet Data", and "Piscivores Diet Data" are the datasets with selected diet categories for each guild without "unidentified diet items" and standardized to 100 proportion which were used for the classification of each major trophic guild into specialized trophic guilds. >></i></p><p><strong>Trophic Network Links Between Specialized Guilds</strong></p><p>The trophic links between fishes and their invertebrate and macrophyte prey groups were identified by our trophic guild classification (Other Guilds Links tab in dataset); however, the trophic role of piscivores is faced with what here we called a "matrioshka paradox", because to know their links with other guilds, we must first know the trophic links of their prey. Moreover, this is not straightforward because the highest taxonomic identification of piscivorous prey is usually limited to family level, which could belong to multiple trophic guilds. This paradox is usually not explicitly stated in the literature, and it is unclear how trophic links have been drawn in previous studies without performing detailed quantitative trophic classifications. Here we estimated the trophic links between piscivorous guilds and the rest of fish guilds by (i) assigning each fish family identified in the diets of piscivorous fishes into their respective specialized guilds based in our trophic classification, (ii) pooling their diet proportions into each specialized trophic guilds they could belong to, (iii) standardizing values by number of species in each piscivorous guild, and (iv) dividing by the total sum of diet proportions to estimate their potential predation (0-100%) on other trophic guilds in the trophic network. Trophic links that had pooled diet proportions with values <5% were discarded for clarity of the network (Piscivores Trophic Links tab in dataset). This information was joined with the trophic information from non-piscivorous trophic guilds and formatted as a list of nodes (guilds and prey groups), and links between nodes (source-target) to create the trophic network of the entire temperate reef fish metacommunity (Nodes Network List and Edges Network Lisk tabs in dataset). All network analyses were done using the computer program for network visualization and analyzes Gephi v0.1 <a href="https://sciwheel.com/work/citation?ids=15257446&pre=&suf=&sa=0">(Bastian et al. 2009)</a>.</p><p><i><< The tabs "Piscivores Trophic Links" and "Other Guilds Links" are datasets containing the calculations of the links between specialized trophic links for Piscivores and other guilds respectively used to create the data of the tabs "Nodes Network List" and "Edges Network List" to create the trophic network of the system of study. >></i></p><p><i><< The tab "Herbivory, Omnivory and Carnivory" contains diet proportion data of all fish species of the study formated to build the barplot (Fig. 4) in the manuscript showing the distribution of consumption of macrophytes, invertebrates and fishes >></i></p><p> </p>
LDA_NEURAL_NETWORK_PATIENT_DATA
Open the record for dataset details and reuse information.
Ischia cGNSS Network - Rinex data quality control (2020)
<p>For each cGNSS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p><ol><li>cGNSS station name (Name),</li><li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GNSS week (Week),</li><li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GNSS week (Week),</li><li>the start and end times of the window (time format is year month day hour min),</li><li>window time laps (Hrs),</li><li>observation interval (OI),</li><li>the number of possible observations (#expt) above the elevation mask,</li><li>the number of complete observations (#obs),</li><li>the ratio of complete to possible observations as a percent (DCP),</li><li>the RMS "multipath combinations" values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP2) rounded to two decimal points,</li><li>cycle slips (CS),</li><li>the ratio of complete observations to cycle slips (obs/CS).</li></ol><p>A full description of cGNSS network is reported in:<br>- De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p><p>Please cite this when using the dataset</p>
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