The structure of natural microbial enemy-victim networks

Poisot, Timothée; Lounnas, Manon; Hochberg, Michael

doi:10.1186/2192-1709-2-13

Cited by 12 publications

(17 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results indicate that when the strength of interactions changes in time because of species coevolution, the feedbacks between ecological and evolutionary dynamics create a trade-off between the number of interacting partners and abundance. This trade-off has already been found in parasite-host and parasitoid-host interactions [83][84][85], and has been related to the high cost of adaptations to multiple victim defences (or exploiter attack) mechanisms [84] (but see [86]). Experiments have also found evidence for a trade-off driven by fluctuating selection when there is rapid coevolution between antagonistic species [87].…”

Section: Discussionmentioning

confidence: 94%

Eco-evolutionary feedbacks promote fluctuating selection and long-term stability of antagonistic networks

2018

View full text Add to dashboard Cite

Studies have shown the potential for rapid adaptation in coevolving populations and that the structure of species interaction networks can modulate the vulnerability of ecological systems to perturbations. Although the feedback loop between population dynamics and coevolution of traits is crucial for understanding long-term stability in ecological assemblages, modelling eco-evolutionary dynamics in species-rich assemblages is still a challenge. We explore how eco-evolutionary feedbacks influence trait evolution and species abundances in 23 empirical antagonistic networks. We show that, if selection due to antagonistic interactions is stronger than other selective pressures, eco-evolutionary feedbacks lead to higher mean species abundances and lower temporal variation in abundances. By contrast, strong selection of antagonistic interactions leads to higher temporal variation of traits and on interaction strengths. Our results present a theoretical link between the study of the species persistence and coevolution in networks of interacting species, pointing out the ways by which coevolution may decrease the vulnerability of species within antagonistic networks to demographic fluctuation.

show abstract

Section: Discussionmentioning

confidence: 94%

Eco-evolutionary feedbacks promote fluctuating selection and long-term stability of antagonistic networks

2018

View full text Add to dashboard Cite

show abstract

“…For example, evidence from Salmonella bacteria and their associated phages on a dairy farm suggests both great diversity of phages, and a high density of multiple specialist phage types, each capable of infecting common bacterial strains (Moreno Switt et al , 2013). A statistical analysis of large phage host range datasets has recently been introduced and applied to bacteria–phage interaction networks from soil (Poisot et al , 2013), the ocean (Flores et al , 2013) and a meta-analysis of 38 laboratory-tested networks (Flores et al , 2011). Overall, these data support the idea that phages in nature span the continuum from specialist to generalist, resulting in what is known as a ‘nested’ structure (reviewed in Weitz et al , 2013; Martiny et al , 2014).…”

Section: Coevolution In the Wildmentioning

confidence: 99%

Bacteria–phage coevolution as a driver of ecological and evolutionary processes in microbial communities

Koskella¹,

Brockhurst²

2014

FEMS Microbiol Rev

698

557

View full text Add to dashboard Cite

Bacteria–phage coevolution, the reciprocal evolution between bacterial hosts and the phages that infect them, is an important driver of ecological and evolutionary processes in microbial communities. There is growing evidence from both laboratory and natural populations that coevolution can maintain phenotypic and genetic diversity, increase the rate of bacterial and phage evolution and divergence, affect community structure, and shape the evolution of ecologically relevant bacterial traits. Although the study of bacteria–phage coevolution is still in its infancy, with open questions regarding the specificity of the interaction, the gene networks of coevolving partners, and the relative importance of the coevolving interaction in complex communities and environments, there have recently been major advancements in the field. In this review, we sum up our current understanding of bacteria–phage coevolution both in the laboratory and in nature, discuss recent findings on both the coevolutionary process itself and the impact of coevolution on bacterial phenotype, diversity and interactions with other species (particularly their eukaryotic hosts), and outline future directions for the field.

show abstract

“…Second, (Type II, Bascompte et al (2003)), the probability of an interaction between animal i and plant j is ( k i / R + k j / C )/2, the average of the richness-standardized degree of both species. In addition, we use the models called Type III in and out (Poisot et al 2013), that use the row-wise and column-wise probability of an interaction respectively, as a way to understand the impact of the degree distribution of upper and lower level species.…”

Section: Methodsmentioning

confidence: 99%

The structure of probabilistic networks

Poisot¹,

Cirtwill²,

Cazelles³

et al. 2015

Preprint

Self Cite

View full text Add to dashboard Cite

1 1. There is a growing realization among community ecologists that interactions between species vary across 2 space and time, and that this variation needs be quantified. Our current numerical framework to analyze the 3 structure of species interactions, based on graph-theoretical approaches, usually do not consider the variabil-4 ity of interactions. Since this variability has been show to hold valuable ecological information, there is a 5 need to adapt the current measures of network structure so that they can exploit it. 6 2. We present analytical expressions of key measures of network structured, adapted so that they account for 7 the variability of ecological interactions. We do so by modeling each interaction as a Bernoulli event; using 8 basic calculus allows expressing the expected value, and when mathematically tractable, its variance. When 9 applied to non-probabilistic data, the measures we present give the same results as their non-probabilistic 10 formulations, meaning that they can be generally applied. 113. We present three case studies that highlight how these measures can be used, in re-analyzing data that exper-12 imentally measured the variability of interactions, to alleviate the computational demands of permutation-13 based approaches, and to use the frequency at which interactions are observed over several locations to infer 14 the structure of local networks. We provide a free and open-source implementation of these measures. 15 4. We discuss how both sampling and data representation of ecological networks can be adapted to allow the 16 application of a fully probabilistic numerical network approach.17

show abstract

The structure of natural microbial enemy-victim networks

Cited by 12 publications

References 73 publications

Eco-evolutionary feedbacks promote fluctuating selection and long-term stability of antagonistic networks

Eco-evolutionary feedbacks promote fluctuating selection and long-term stability of antagonistic networks

Bacteria–phage coevolution as a driver of ecological and evolutionary processes in microbial communities

The structure of probabilistic networks

Contact Info

Product

Resources

About