Contrasting effects of land‐use changes on herbivory and pollination networks

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Implications of incomplete networks on estimation of landscape genetic connectivity

Understanding processes and landscape features governing connectivity among individuals and populations is fundamental to many ecological, evolutionary, and conservation questions. Network analyses based on graph theory are emerging as a prominent approach to quantify patterns of connectivity with more recent applications in landscape genetics aimed at understanding the influence of landscape features on gene flow. Despite the strong conceptual framework of graph theory, the effect of incomplete networks resulting from missing nodes (i.e. populations) and their genetic connectivity network interactions on landscape genetic inferences remains unknown. We tested the violation of this assumption by subsampling from a known complete network of breeding ponds of the Columbia Spotted Frog (Rana luteiventris) in the Bighorn Crags (Idaho, USA). Variation in the proportion of missing nodes strongly influenced node-level centrality indices, whereas indices describing network-level properties were more robust. Overall incomplete networks combined with network algorithm types used to link nodes appears to be critical to the rank-order sensitivity of centrality indices and to the Mantel-based inferences made regarding the role of landscape features on gene flow. Our findings stress the importance of sampling effort and topological network structure as they both affect the estimation of genetic connectivity. Given that failing to account for uncertainty on network outcomes can lead to quantitatively different conclusions, we recommend the routine application of sensitivity analyses to network inputs and assumptions.     

Motifs in bipartite ecological networks: uncovering indirect interactions

Indirect interactions play an essential role in governing population, community and coevolutionary dynamics across a diverse range of ecological communities. Such communities are widely represented as bipartite networks: graphs depicting interactions between two groups of species, such as plants and pollinators or hosts and parasites. For over thirty years, studies have used indices, such as connectance and species degree, to characterise the structure of these networks and the roles of their constituent species. However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. Given the large literature demonstrating the importance and ubiquity of indirect effects, many studies of network structure are likely missing a substantial piece of the ecological puzzle. Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. While this framework is a significant departure from the current way of thinking about bipartite ecological networks, we show that this shift is supported by analyses of simulated and empirical data. We use simulations to show how consideration of indirect interactions can highlight differences missed by the current index paradigm that may be ecologically important. We extend this finding to empirical plant–pollinator communities, showing how two bee species, with similar direct interactions, differ in how specialised their competitors are. These examples underscore the need to not rely solely on network‐ and species‐level indices for characterising the structure of bipartite ecological networks.     

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks

Background  

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure.     

Succinctly assessing the topological importance of species in flower–pollinator networks

The topological importance of species within networks is an important way of bringing a species-level consideration to the study of whole ecological networks. There are many different indices of topological importance, including centrality indices, but it is likely that a small number are sufficient to explain variation in topological importance. We used 14 indices to describe topological importance of plants and pollinators in 12 quantitative mutualistic (plant–pollinator) networks. The 14 indices varied in their consideration of interaction strength (weighted versus unweighted indices) and indirect interactions (from the local measure of degree to meso-scale indices). We use principal components approximation to assess how well every combination of 1–14 indices approximated to the results of principal components analysis (PCA). We found that one or two indices were sufficient to explain up to 90% of the variation in topological importance in both plants and pollinators. The choice of index was crucial because there was considerable variation between the best and the worst approximating subsets of indices. The best single indices were unweighted degree and unweighted topological importance (Jordán's TI index) with two steps (a measurement of apparent competition). The best pairs of indices consisted of a measure of a TI index and one of closeness centrality (weighted or unweighted) or d′ (a standardised species-level measure of partner diversity). Although we have found indices that efficiently explain variation in topological importance, we recommend further research to discover the real-world relevance of different aspects of topological importance to species in ecological networks.     

Making Large-Scale Networks from fMRI Data

Pairwise correlations are currently a popular way to estimate a large-scale network (> 1000 nodes) from functional magnetic resonance imaging data. However, this approach generally results in a poor representation of the true underlying network. The reason is that pairwise correlations cannot distinguish between direct and indirect connectivity. As a result, pairwise correlation networks can lead to fallacious conclusions; for example, one may conclude that a network is a small-world when it is not. In a simulation study and an application to resting-state fMRI data, we compare the performance of pairwise correlations in large-scale networks (2000 nodes) against three other methods that are designed to filter out indirect connections. Recovery methods are evaluated in four simulated network topologies (small world or not, scale-free or not) in scenarios where the number of observations is very small compared to the number of nodes. Simulations clearly show that pairwise correlation networks are fragmented into separate unconnected components with excessive connectedness within components. This often leads to erroneous estimates of network metrics, like small-world structures or low betweenness centrality, and produces too many low-degree nodes. We conclude that using partial correlations, informed by a sparseness penalty, results in more accurate networks and corresponding metrics than pairwise correlation networks. However, even with these methods, the presence of hubs in the generating network can be problematic if the number of observations is too small. Additionally, we show for resting-state fMRI that partial correlations are more robust than correlations to different parcellation sets and to different lengths of time-series.     

A framework to find the logic backbone of a biological network

Background

Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network.

Results

In this work, we propose a simple logic framework, based on categorizing causal relationships as sufficient or necessary, as a complement to Boolean networks. We identify and explore the properties of complex subnetworks that are distillable into a single logic relationship. We also identify cyclic subnetworks that ensure the stabilization of the state of participating nodes regardless of the rest of the network. We identify the logic backbone of biomolecular networks, consisting of external signals, self-sustaining cyclic subnetworks (stable motifs), and output nodes. Furthermore, we use the logic framework to identify crucial nodes whose override can drive the system from one steady state to another. We apply these techniques to two biological networks: the epithelial-to-mesenchymal transition network corresponding to a developmental process exploited in tumor invasion, and the network of abscisic acid induced stomatal closure in plants. We find interesting subnetworks with logical implications in these networks. Using these subgraphs and motifs, we efficiently reduce both networks to succinct backbone structures.

Conclusions

The logic representation identifies the causal relationships between distant nodes and subnetworks. This knowledge can form the basis of network control or used in the reverse engineering of networks.

     

Pattern of functional extinctions in ecological networks with a variety of interaction types

There is a strong trend of declining populations in many species of both animals and plants. Dwindling numbers of species can eventually lead to their functional extinction. Functional, or ecological, extinction occurs when a species becomes too rare to fulfill its ecological, interactive role in the ecosystem, leading to true (numerical) extinction of other depending species. Recent theoretical work on food webs suggests that the frequency of functional extinction might be surprisingly high. However, little is known about the risk of functional species extinctions in networks with other types of interactions than trophic ones. Here, we explore the frequency of functional extinctions in model ecological networks having different proportions of antagonistic and mutualistic links. Furthermore, we investigate the topological relationship between functionally and numerically extinct species. We find that (1) the frequency of functional extinctions is higher in networks containing a mixture of antagonistic and mutualistic interactions than in networks with only one type of interaction, (2) increased mortality rate of species having both mutualistic and antagonistic links is more likely to lead to extinction of another species than to extinction of the species itself compared to species having only mutualistic or antagonistic links, and (3) trophic distance (shortest path) between functionally and numerically extinct species is, on average, longer than one, indicating the importance of indirect effects. These results generalize the findings of an earlier study on food webs, demonstrating the potential importance of functional extinction in a variety of ecological network types.     

Approaches to integrating genetic data into ecological networks

As molecular tools for assessing trophic interactions become common, research is increasingly focused on the construction of interaction networks. Here, we demonstrate three key methods for incorporating DNA data into network ecology and discuss analytical considerations using a model consisting of plants, insects, bats and their parasites from the Costa Rica dry forest. The simplest method involves the use of Sanger sequencing to acquire long sequences to validate or refine field identifications, for example of bats and their parasites, where one specimen yields one sequence and one identification. This method can be fully quantified and resolved and these data resemble traditional ecological networks. For more complex taxonomic identifications, we target multiple DNA loci, for example from a seed or fruit pulp sample in faeces. These networks are also well resolved but gene targets vary in resolution and quantification is difficult. Finally, for mixed templates such as faecal contents of insectivorous bats, we use DNA metabarcoding targeting two sequence lengths (157 and 407 bp) of one gene region and a MOTU, BLAST and BIN association approach to resolve nodes. This network type is complex to generate and analyse, and we discuss the implications of this type of resolution on network analysis. Using these data, we construct the first molecular‐based network of networks containing 3,304 interactions between 762 nodes of eight trophic functions and involving parasitic, mutualistic and predatory interactions. We provide a comparison of the relative strengths and weaknesses of these data types in network ecology.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Topological keystone species: measures of positional importance in food webs

The local extinction or large fluctuation in abundance of a species may seriously affect other species in the community. The effects spread through the community by direct and indirect interactions. The network perspective on ecology can help map the pathways of these effects, for food webs, the pathways of indirect trophic interactions. Indirect interactions typically decay in intensity as they spread. Therefore, there is a conceptual maximum range in topological space beyond which interactions have no effects, even though all species remain connected. Neither the local characteristics of species, nor the global characteristics of entire webs, suitably quantify this range. We therefore apply intermediate scale indices that reflect the limitations imposed by effect damping in networks. We present a complex analysis of the topological positional importance of species in the Chesapeake Bay web. This web is a carbon-flow network that represents trophic interactions. We present several different indices reflecting different properties and discuss which questions the different indices best answer. We look for the best indices for identifying the key players in ecosystem functioning. Our study contributes to the quantification of relative species importance and provides an exact and a priori determination of a class of candidate keystone species that can inform applied and conservation ecology as well as theoretical concerns.     

The role of the endemic and critically endangered Colorful Puffleg Eriocnemis mirabilis in plant‐hummingbird networks of the Colombian Andes

Ecological network approaches may contribute to conservation practices by quantifying within‐community importance of species. In mutualistic plant‐pollinator systems, such networks reflect potential pollination of the plants and a considerable portion of the energy consumption by the pollinators, two key components for each party. Here, we used two different sampling approaches to describe mutualistic plant‐hummingbird networks from a cloud forest in the Colombian Western Andes, home to the Colorful Puffleg Eriocnemis mirabilis, an endemic and critically endangered hummingbird. We contrast networks between two localities (a protected area inside a National park vs. its buffer zone) and across sampling methods (floral visitation vs. pollen loads) to assess how the network structure and the importance of each hummingbird species within the networks may change. Visitation networks were characterized as having higher sampling completeness, yet pollen load network recorded more pollen types than plant species recorded by visitation. Irrespective of the sampling methods, the Colorful Puffleg was one of the most important hummingbird species in the network within the protected area inside the National park, but not in the buffer zone. Moreover, most species‐level network indices were related to hummingbirds’ abundance. This suggests that conservation initiatives aimed at the endangered Colorful Puffleg may both help on the survival of this endangered hummingbird, as well as on maintaining its key role in the mutualistic interaction network inside the National Park. Our study illustrates how conservation practitioners could assess the local importance of endangered species using interaction network approaches.     

Genetic influences on social dominance: cow wars

Analyzing population dynamics in changing habitats is a prerequisite for population dynamics forecasting. The recent development of metapopulation modeling allows the estimation of dispersal kernels based on the colonization pattern but the accuracy of these estimates compared with direct estimates of the seed dispersal kernel has rarely been assessed. In this study, we used recent genetic methods based on parentage analysis (spatially explicit mating models) to estimate seed and pollen dispersal kernels as well as seed and pollen immigration in fragmented urban populations of the plant species Crepis sancta with contrasting patch dynamics. Using two independent networks, we documented substantial seed immigration and a highly restricted dispersal kernel. Moreover, immigration heterogeneity among networks was consistent with previously reported metapopulation dynamics, showing that colonization was mainly due to external colonization in the first network (propagule rain) and local colonization in the second network. We concluded that the differences in urban patch dynamics are mainly due to seed immigration heterogeneity, highlighting the importance of external population source in the spatio-temporal dynamics of plants in a fragmented landscape. The results show that indirect and direct methods were qualitatively consistent, providing a proper interpretation of indirect estimates. This study provides attempts to link genetic and demographic methods and show that patch occupancy models may provide simple methods for analyzing population dynamics in heterogeneous landscapes in the context of global change.     

Labeling Nodes Using Three Degrees of Propagation

The properties (or labels) of nodes in networks can often be predicted based on their proximity and their connections to other labeled nodes. So-called “label propagation algorithms” predict the labels of unlabeled nodes by propagating information about local label density iteratively through the network. These algorithms are fast, simple and scale to large networks but nonetheless regularly perform better than slower and much more complex algorithms on benchmark problems. We show here, however, that these algorithms have an intrinsic limitation that prevents them from adapting to some common patterns of network node labeling; we introduce a new algorithm, 3Prop, that retains all their advantages but is much more adaptive. As we show, 3Prop performs very well on node labeling problems ill-suited to label propagation, including predicting gene function in protein and genetic interaction networks and gender in friendship networks, and also performs slightly better on problems already well-suited to label propagation such as labeling blogs and patents based on their citation networks. 3Prop gains its adaptability by assigning separate weights to label information from different steps of the propagation. Surprisingly, we found that for many networks, the third iteration of label propagation receives a negative weight.

Availability

The code is available from the authors by request.     

A comparative social network analysis of wasp colonies and classrooms: Linking network structure to functioning

A major question in current network science is how to understand the relationship between structure and functioning of real networks. Here we present a comparative network analysis of 48 wasp and 36 human social networks. We have compared the centralisation and small world character of these interaction networks and have studied how these properties change over time. We compared the interaction networks of (1) two congeneric wasp species (Ropalidia marginata and Ropalidia cyathiformis), (2) the queen-right (with the queen) and queen-less (without the queen) networks of wasps, (3) the four network types obtained by combining (1) and (2) above, and (4) wasp networks with the social networks of children in 36 classrooms. We have found perfect (100%) centralisation in a queen-less wasp colony and nearly perfect centralisation in several other queen-less wasp colonies. Note that the perfectly centralised interaction network is quite unique in the literature of real-world networks. Differences between the interaction networks of the two wasp species are smaller than differences between the networks describing their different colony conditions. Also, the differences between different colony conditions are larger than the differences between wasp and children networks. For example, the structure of queen-right R. marginata colonies is more similar to children social networks than to that of their queen-less colonies. We conclude that network architecture depends more on the functioning of the particular community than on taxonomic differences (either between two wasp species or between wasps and humans).