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opencc-zeroDec 2018View details →

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.

opencc-zeroDec 2014View details →

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.

opencc-zeroDec 2013View details →

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.

opencc-by-4.0Nov 2023View details →

Data from: Refining the trophic diversity, ecological network structure, and bottom-up importance of prey groups for temperate reef fishes

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: Refining the trophic diversity, ecological network structure, and bottom-up importance of prey groups for temperate reef fishes: <a href="https://doi.org/10.32942/X2CC97">https://doi.org/10.32942/X2CC97</a>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>DATASET DESCRIPTIONRegion of StudyThe 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>Species CompositionThe 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.Trophic InformationAll 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 = (name of species* OR *common name of species*) 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>.<< 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 >>Trophic guilds classificationTo 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 vegdist 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 diana (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 (e.g. 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>.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.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 metaMDS 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 adonis2 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 pairwise.adonis2 of the R package pairwiseAdonis <a href="https://sciwheel.com/work/citation?ids=15190336&pre=&suf=&sa=0">(Martinez 2017)</a>.<< 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. >>Trophic Network Links Between Specialized GuildsThe 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>.<< 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. >><< 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 >>

opencc-by-4.0Apr 2024View details →

Supplementary data to BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell RNA-seq data imputation

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opencc-by-4.0Nov 2024View details →

structural transitions in Asia-Pacific trade networks: from the us-china trade war to the covid-19 pandemic

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opencc-zeroJan 2022View details →

Data from: Spatial familial networks to infer demographic structure of wild populations

In social species, reproductive success and rates of dispersal vary among individuals resulting in spatially structured populations. Network analyses of familial relationships may provide insights on how these parameters influence population-level demographic patterns. These methods have however rarely been applied to genetically-derived pedigree data from wild populations. Here we use parent-offspring relationships to construct familial networks from polygamous boreal woodland caribou (Rangifer tarandus caribou) in Saskatchewan, Canada, to inform recovery efforts. We collected samples from 933 individuals at 15 variable microsatellite loci along with caribou-specific primers for sex identification. Using network measures, we assess the contribution of individual caribou to the population with several centrality measures and then determine which measures are best suited to inform on the population demographic structure. We investigate the centrality of individuals from eighteen different local areas, along with the entire population. We found substantial differences in centrality of individuals in different local areas, that in turn contributed differently to the full network, highlighting the importance of analyzing networks at different scales. The full network revealed that boreal caribou in Saskatchewan form a complex, interconnected familial network, as the removal of edges with high betweenness did not result in distinct subgroups. Alpha, betweenness, and eccentricity centrality were the most informative measures to characterize the population demographic structure and for spatially identifying areas of highest fitness levels and family cohesion across the range. We found varied levels of dispersal, fitness and cohesion in family groups. Synthesis and applications: Our results demonstrate the value of different network measures in assessing genetically-derived familial networks. The spatial application of the familial networks identified individuals presenting different fitness levels, short and long-distance dispersing ability across the range in support of population monitoring and recovery efforts.

opencc-zeroFeb 2022View details →

Data from: Anderson lab experiments from synthesizing the effects of spatial network structure on predator prey dynamics

Predator-prey persistence is thought to be enhanced by spatial heterogeneity. Theory predicts that metacommunity size, spatial connectivity, network synchrony, predator identity, and productivity influence predator-prey persistence, through a variety of mechanisms such as statistical stabilization, colonization-extinction dynamics, and trophic interactions. However, comparative tests and synthesis of the multiple factors and mechanisms across different spatial networks are needed to understand which factors and mechanisms of spatial network structure promote predator-prey persistence. To address this gap between theory and empirical work, we synthesized data from 22 microcosm experiments of protist predator-prey communities differing the productivity, connectivity, and size of spatial habitat structure. Prey time to extinction was better explained by productivity and spatial factors than predator time to extinction. At the local and regional scale, metacommunity size and productivity had positive effects on prey occupancy, whereas connectivity negatively influenced prey occupancy. For predators, metacommunity size and connectivity had positive effects on predator occupancy, network synchrony had negative influences, and productivity showed a hump-shaped relationship with predator occupancy. Further, trophic interactions drove variation in the way species were spatially structured, where the strength and direction of predator and prey occupancy relationships varied among productivity levels and predator-prey combinations. In predator-prey interactions that were stronger, prey occupancy showed negative relationship with predator occupancy regardless of productivity. However, in predator-prey interactions that were weaker, prey occupancy was positively related to predator occupancy at low productivity, and this relationship disappeared at higher productivity treatments where prey occupancy was high regardless of predator occupancy. Predictions from metapopulation theory explained predator occupancy, while prey were better explained by trophic dynamics. Taken together, these results highlight that spatial network structure has a complex, spatially contingent relationship with predator-prey dynamics.

opencc-by-4.0Apr 2024View details →

Figure 2 of the paper "Multi-level structure of the First Tuesday communities after the 2000 dot-com crash: A social network analysis of economic actors based on web archives"

An example of a First Tuesday meeting held in Riga in December 2001.

opencc-by-4.0Jul 2024View details →

ChEMBL Data for 'Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models'

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opencc-zeroJul 2019View details →

Supplementary material 1 from: Matsuoka S, Sugiyama Y, Sato H, Katano I, Harada K, Doi H (2019) Spatial structure of fungal DNA assemblages revealed with eDNA metabarcoding in a forest river network in western Japan. Metabarcoding and Metagenomics 3: e36335. https://doi.org/10.3897/mbmg.3.36335

: Data type: multimedia

opencc-zeroJul 2019View details →

Supplementary material 2 from: Matsuoka S, Sugiyama Y, Sato H, Katano I, Harada K, Doi H (2019) Spatial structure of fungal DNA assemblages revealed with eDNA metabarcoding in a forest river network in western Japan. Metabarcoding and Metagenomics 3: e36335. https://doi.org/10.3897/mbmg.3.36335

: Data type: molecular data

opencc-zeroDec 2017View details →

Data from: Local extinctions of obligate frugivores and patch size reduction disrupt the structure of seed dispersal networks

A central problem in ecology is to understand how human impacts affect plant-animal interactions that lead to effective seed dispersal services for plant communities. Seed dispersal services are the outcome of plant-frugivore interactions that often form local networks of interacting species. Recent work has shown that some frugivorous bird species are more critical to network organization than others. Here, we explore how patch size and the potential local extinctions of obligate frugivorous birds affect the reorganization of seed dispersal networks. We examined the structure of 20 empirical seed dispersal networks documented across tropical avian assemblages occupying widely variable habitat patch sizes, a surrogate of the amount of remaining habitat. Networks within small forest patches consistently supported both lower plant and bird species richness. Forest patch size was positively associated with nestedness, indicating that reductions in patch size disrupted the nested organization of seed dispersal networks. Obligate frugivores, especially large-bodied species, were almost entirely absent from small forest patches. Analysis at the species level showed that obligate frugivores formed the core of interacting species, connecting species within a given seed dispersal network. Our combined results revealed that patch size reduction erodes frugivorous bird diversity, thereby affecting the integrity of seed dispersal networks. We highlight the importance of conserving large forest patches to maintain tropical forest functionality.

opencc-by-4.0Jul 2021View details →

A new structural theory of the elasticity of particle-filled networks

A new structural theory of the elasticity of particle-filled networks

opencc-zeroOct 2021View details →

Data from: Elements of metacommunity structure of diatoms and macroinvertebrates within stream networks differing in environmental heterogeneity

Aim: Idealized metacommunity structures (i.e. checkerboard, random, quasi-structures, nested, Clementsian, Gleasonian, and evenly spaced) have recently gained increasing attention, but their relationships with environmental heterogeneity and how they vary with organism groups remain poorly understood. Here we tested two main hypotheses: (1) gradient-driven patterns (Clementsian and Gleasonian) occur frequently in heterogeneous environments, and (2) small organisms (here, diatoms) are more likely to exhibit gradient-driven patterns than large organisms (here, macroinvertebrates). Location: Streams in three regions in China. Taxon: Diatoms and macroinvertebrates. Methods: The stream diatom and macroinvertebrate data, as well as the environmental data collected from the same set of sites were used to examine the idealized metacommunity structures via the elements of the metacommunity structure (EMS; coherence, turnover, and boundary clumping) analysis in three regions. We extended the traditional EMS approach by ordering sites along known environmental gradients. Results: We found that Clementsian structure with high degrees of coherence and turnover, and significantly positive clumping was typically observed in the high-heterogeneity regions, whereas randomness was prevalent in the low-heterogeneity region. Macroinvertebrates exhibited clearer Clementsian structures compared with diatoms, while diatoms showed more randomness compared with macroinvertebrates, indicating a stronger role of environmental filtering for macroinvertebrates than diatoms. In most cases, the results of the more novel EMS approach differed from the results of the traditional EMS technique. Main Conclusions: Our results suggested that the occurrence of different metacommunity structures may be related with the degree of regional environmental heterogeneity. However, diatom metacommunities were more random than those of macroinvertebrate, and such an unexpected result may result from different dispersal abilities between the two organism groups. In addition, we found that the novel EMS approach increased power in discerning metacommunity structure in comparison to the traditional EMS technique.

Fig. 5 in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 5. Results of three channel MRM scans using UPLC conditions for the TQD system.

Fig. 1 in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 1. Structure of isolated imidazole, amidine and β-carboline alkaloids.

Fig. 3 in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 3. Structures of the proposed 'macaridine' (left) and macapyrrolin C (right).

Fig. 2. Key COSY and HMBC correlations observed for lepidiline E in Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Fig. 2. Key COSY and HMBC correlations observed for lepidiline E (1) F (2) and G (3).