Anton H. Westveld

Modeling ecological drivers in marine viral communities using comparative metagenomics and network analyses

Significance Microorganisms and their viruses are increasingly recognized as drivers of myriad ecosystem processes. However, our knowledge of their roles is limited by the inability of culture-dependent and culture-independent (e.g., metagenomics) methods to be fully implemented at scales relevant to the diversity found in nature. Here we combine advances in bioinformatics (shared k-mer analyses) and social networking (regression modeling) to develop an annotation- and assembly-free visualization and analytical strategy for comparative metagenomics that uses all the data in a unified statistical framework. Application to 32 Pacific Ocean viromes, the first large-scale quantitative viral metagenomic dataset, tested existing and generated further hypotheses about ecological drivers of viral community structure. Highly computationally scalable, this new approach enables diverse sequence-based large-scale comparative studies. Long-standing questions in marine viral ecology are centered on understanding how viral assemblages change along gradients in space and time. However, investigating these fundamental ecological questions has been challenging due to incomplete representation of naturally occurring viral diversity in single gene- or morphology-based studies and an inability to identify up to 90% of reads in viral metagenomes (viromes). Although protein clustering techniques provide a significant advance by helping organize this unknown metagenomic sequence space, they typically use only ∼75% of the data and rely on assembly methods not yet tuned for naturally occurring sequence variation. Here, we introduce an annotation- and assembly-free strategy for comparative metagenomics that combines shared k-mer and social network analyses (regression modeling). This robust statistical framework enables visualization of complex sample networks and determination of ecological factors driving community structure. Application to 32 viromes from the Pacific Ocean Virome dataset identified clusters of samples broadly delineated by photic zone and revealed that geographic region, depth, and proximity to shore were significant predictors of community structure. Within subsets of this dataset, depth, season, and oxygen concentration were significant drivers of viral community structure at a single open ocean station, whereas variability along onshore–offshore transects was driven by oxygen concentration in an area with an oxygen minimum zone and not depth or proximity to shore, as might be expected. Together these results demonstrate that this highly scalable approach using complete metagenomic network-based comparisons can both test and generate hypotheses for ecological investigation of viral and microbial communities in nature.

A mixed effects model for longitudinal relational and network data, with applications to international trade and conflict

The focus of this paper is an approach to the modeling of longitudinal social network or relational data. Such data arise from measurements on pairs of objects or actors made at regular temporal intervals, resulting in a social network for each point in time. In this article we represent the network and temporal dependencies with a random effects model, resulting in a stochastic process defined by a set of stationary covariance matrices. Our approach builds upon the social relations models of Warner, Kenny and Stoto [Journal of Personality and Social Psychology 37 (1979) 1742--1757] and Gill and Swartz [Canad. J. Statist. 29 (2001) 321--331] and allows for an intra- and inter-temporal representation of network structures. We apply the methodology to two longitudinal data sets: international trade (continuous response) and militarized interstate disputes (binary response).

A unifying approach for food webs, phylogeny, social networks, and statistics

A food web consists of nodes, each consisting of one or more species. The role of each node as predator or prey determines the trophic relations that weave the web. Much effort in trophic food web research is given to understand the connectivity structure, or the nature and degree of dependence among nodes. Social network analysis (SNA) techniques—quantitative methods commonly used in the social sciences to understand network relational structure—have been used for this purpose, although postanalysis effort or biological theory is still required to determine what natural factors contribute to the feeding behavior. Thus, a conventional SNA alone provides limited insight into trophic structure. Here we show that by using novel statistical modeling methodologies to express network links as the random response of within- and internode characteristics (predictors), we gain a much deeper understanding of food web structure and its contributing factors through a unified statistical SNA. We do so for eight empirical food webs: Phylogeny is shown to have nontrivial influence on trophic relations in many webs, and for each web trophic clustering based on feeding activity and on feeding preference can differ substantially. These and other conclusions about network features are purely empirical, based entirely on observed network attributes while accounting for biological information built directly into the model. Thus, statistical SNA techniques, through statistical inference for feeding activity and preference, provide an alternative perspective of trophic clustering to yield comprehensive insight into food web structure.

A statistical social network model for consumption data in trophic food webs

Abstract We adapt existing statistical modeling techniques for social networks to study consumption data observed in trophic food webs. These data describe the feeding volume (non-negative) among organisms grouped into nodes, called trophic species, that form the food web. Model complexity arises due to the extensive amount of zeros in the data, as each node in the web is predator/prey to only a small number of other trophic species. Many of the zeros are regarded as structural (non-random) in the context of feeding behavior. The presence of basal prey and top predator nodes (those who never consume and those who are never consumed, with probability 1) creates additional complexity to the statistical modeling. We develop a special statistical social network model to account for such network features. The model is applied to two empirical food webs; focus is on the web for which the population size of seals is of concern to various commercial fisheries.

A Statistical View of Learning in the Centipede Game

In this article we evaluate the statistical evidence that a population of students learn about the sub-game perfect Nash equilibrium of the centipede game via repeated play of the game. This is done by formulating a model in which a players error in assessing the utility of decisions changes as they gain experience with the game. We first estimate parameters in a statistical model where the probabilities of choices of the players are given by a Quantal Response Equilibrium (QRE) (McKelvey and Palfrey, 1995, 1996, 1998), but are allowed to change with repeated play. This model gives a better fit to the data than similar models previously considered. However, substantial correlation of outcomes of games having a common player suggests that a statistical model that captures within-subject correlation is more appropriate. Thus we then estimate parameters in a model which allows for within-player correlation of decisions and rates of learning. Through out the paper we also consider and compare the use of randomization tests and posterior predictive tests in the context of exploratory and confirmatory data analyses.

Modeling of the learning process in centipede games: Modeling of learning in centipede games

In this article we evaluate the statistical evidence that a population of students learn about the sub-game perfect Nash equilibrium of the centipede game via repeated play of the game. This is done by formulating a model in which a players error in assessing the utility of decisions changes as they gain experience with the game. We first estimate parameters in a statistical model where the probabilities of choices of the players are given by a Quantal Response Equilibrium (QRE) (McKelvey and Palfrey, 1995, 1996, 1998), but are allowed to change with repeated play. This model gives a better fit to the data than similar models previously considered. However, substantial correlation of outcomes of games having a common player suggests that a statistical model that captures within-subject correlation is more appropriate. Thus we then estimate parameters in a model which allows for within-player correlation of decisions and rates of learning. Through out the paper we also consider and compare the use of randomization tests and posterior predictive tests in the context of exploratory and confirmatory data analyses.

Modeling of the learning process in centipede games

In this article we evaluate the statistical evidence that a population of students learn about the sub-game perfect Nash equilibrium of the centipede game via repeated play of the game. This is done by formulating a model in which a players error in assessing the utility of decisions changes as they gain experience with the game. We first estimate parameters in a statistical model where the probabilities of choices of the players are given by a Quantal Response Equilibrium (QRE) (McKelvey and Palfrey, 1995, 1996, 1998), but are allowed to change with repeated play. This model gives a better fit to the data than similar models previously considered. However, substantial correlation of outcomes of games having a common player suggests that a statistical model that captures within-subject correlation is more appropriate. Thus we then estimate parameters in a model which allows for within-player correlation of decisions and rates of learning. Through out the paper we also consider and compare the use of randomization tests and posterior predictive tests in the context of exploratory and confirmatory data analyses.