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

Chiu, Grace S.; Westveld, Anton H.

doi:10.1073/pnas.1015359108

Cited by 23 publications

(37 citation statements)

References 29 publications

(29 reference statements)

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“…For example, Chiu and Westveld (35) recently used such models to understand the role of phylogeny in food web structure. Such models may not only advance our understanding of the causes and consequences of ecological network structure but may also improve conservation and management strategies that rely on network inferences.…”

Section: Discussionmentioning

confidence: 99%

Social network models predict movement and connectivity in ecological landscapes

Fletcher¹,

Acevedo²,

Reichert³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.dispersal | graph theory | habitat fragmentation | latent space models | landscape ecology N etwork analysis has recently exploded across scientific disciplines, including the social sciences, physics, cellular biology, and ecology (1-4). Topics as divergent as the stability of the Internet and the structure of metabolic reactions can be depicted through network analysis (1, 3). Such analysis is beneficial because it can facilitate the identification of complex, and often emergent, patterns, and can provide hypotheses for relationships between structure and function in many systems (2, 3). Nonetheless, a growing, widespread concern in the topic of network analysis is the reliability of data used in constructing networks (4-8).In ecology and conservation, network analysis is increasingly being used to assess population connectivity across landscapes (9-13). Because of the importance of connectivity in conservation and its relevance to population and community ecology (14-16), network analysis and the accompanying use of graph theory are often emphasized as powerful approaches that have modest data requirements for assessing connectivity (10,11,13). In this spatial context, resource patches are considered nodes (or vertices) and movements and/or flows between pat...

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Section: Discussionmentioning

confidence: 99%

Social network models predict movement and connectivity in ecological landscapes

Fletcher¹,

Acevedo²,

Reichert³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…To model the valued (nonbinary) nondirected data above, we consider the latent space approach outlined in Eq. 1 (23)(24)(25)53). Our network modeling framework, via random effects, decomposes the statistical variation in the data to account for (i) the activity level ða i Þ of each virome i (average amount of sequence space shared across the network for each virome i) and (ii) similarity (clustering) of shared sequence amount among viromes.…”

Section: Methodsmentioning

confidence: 99%

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

Hurwitz

Westveld

Brum

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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100

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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.virus | microbial ecology | Bayesian network M icroorganisms drive global biogeochemical cycles (1), with abundances and taxonomic composition tuned to spatiotemporally varying environmental conditions (2-5). Viruses then modulate these biogeochemical processes through mortality, horizontal gene transfer, and metabolic reprogramming (6). However, our understanding of how viral communities change in response to biological, physical, and chemical factors and host availability has been limited by technical challenges.Most viruses in the ocean lack both cultivated representatives [85% of 1,100 sequenced phage genomes derive from only 3 of 45 bacterial phyla (7)] and a universally conserved marker gene (8); thus, metagenomics is commonly applied to characterize the ecology and evolution of viral assemblages. Problematically, however, our ability to investigate these assemblages via metagenomics remains limited by the lack of known viruses and viral proteins in biological sequence databases. The first viral metagenome (virome) used thousands of Sanger reads and found that 65% of sequences were unknown [i.e., no database match for reads >600 bp (9)]. Adoption of next-generation sequencing (NGS) technologies then generated hundreds...

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“…The inference for in turn accounted for these imputed values (Fig 4). Note that the covariates were log-transformed to reduce skewness (S5 File), then subsequently centered to improve computational efficiency ([42–44] and S5 File). See S4 File for the roles of model parameters, observed data, and prior and posterior distributions in Bayesian inference, and for detailed model statements for our studies.…”

Section: Methodsmentioning

confidence: 99%

Influences of Host Community Characteristics on Borrelia burgdorferi Infection Prevalence in Blacklegged Ticks

et al. 2017

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Lyme disease is a major vector-borne bacterial disease in the USA. The disease is caused by Borrelia burgdorferi, and transmitted among hosts and humans, primarily by blacklegged ticks (Ixodes scapularis). The ~25 B. burgdorferi genotypes, based on genotypic variation of their outer surface protein C (ospC), can be phenotypically separated as strains that primarily cause human diseases—human invasive strains (HIS)—or those that rarely do. Additionally, the genotypes are non-randomly associated with host species. The goal of this study was to examine the extent to which phenotypic outcomes of B. burgdorferi could be explained by the host communities fed upon by blacklegged ticks. In 2006 and 2009, we determined the host community composition based on abundance estimates of the vertebrate hosts, and collected host-seeking nymphal ticks in 2007 and 2010 to determine the ospC genotypes within infected ticks. We regressed instances of B. burgdorferi phenotypes on site-specific characteristics of host communities by constructing Bayesian hierarchical models that properly handled missing data. The models provided quantitative support for the relevance of host composition on Lyme disease risk pertaining to B. burgdorferi prevalence (i.e. overall nymphal infection prevalence, or NIPAll) and HIS prevalence among the infected ticks (NIPHIS). In each year, NIPAll and NIPHIS was found to be associated with host relative abundances and diversity. For mice and chipmunks, the association with NIPAll was positive, but tended to be negative with NIPHIS in both years. However, the direction of association between shrew relative abundance with NIPAll or NIPHIS differed across the two years. And, diversity (H') had a negative association with NIPAll, but positive association with NIPHIS in both years. Our analyses highlight that the relationships between the relative abundances of three primary hosts and the community diversity with NIPAll, and NIPHIS, are variable in time and space, and that disease risk inference, based on the role of host community, changes when we examine risk overall or at the phenotypic level. Our discussion focuses on the observed relationships between prevalence and host community characteristics and how they substantiate the ecological understanding of phenotypic Lyme disease risk.

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

Cited by 23 publications

References 29 publications

Social network models predict movement and connectivity in ecological landscapes

Social network models predict movement and connectivity in ecological landscapes

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

Influences of Host Community Characteristics on Borrelia burgdorferi Infection Prevalence in Blacklegged Ticks

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