Dataset Information

Assembly of complex plant-fungus networks.

ABSTRACT: Species in ecological communities build complex webs of interaction. Although revealing the architecture of these networks is fundamental to understanding ecological and evolutionary dynamics in nature, it has been difficult to characterize the structure of most species-rich ecological systems. By overcoming this limitation through next-generation sequencing technology, we herein uncover the network architecture of below-ground plant-fungus symbioses, which are ubiquitous to terrestrial ecosystems. The examined symbiotic network of a temperate forest in Japan includes 33 plant species and 387 functionally and phylogenetically diverse fungal taxa, and the overall network architecture differs fundamentally from that of other ecological networks. In contrast to results for other ecological networks and theoretical predictions for symbiotic networks, the plant-fungus network shows moderate or relatively low levels of interaction specialization and modularity and an unusual pattern of 'nested' network architecture. These results suggest that species-rich ecological networks are more architecturally diverse than previously recognized.

SUBMITTER: Toju H

PROVIDER: S-EPMC4218951 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Assembly of complex plant-fungus networks.

Toju Hirokazu H Guimarães Paulo R PR Olesen Jens M JM Olesen Jens M JM Thompson John N JN

Nature communications 20141020

Species in ecological communities build complex webs of interaction. Although revealing the architecture of these networks is fundamental to understanding ecological and evolutionary dynamics in nature, it has been difficult to characterize the structure of most species-rich ecological systems. By overcoming this limitation through next-generation sequencing technology, we herein uncover the network architecture of below-ground plant-fungus symbioses, which are ubiquitous to terrestrial ecosyste ...[more]

PMID: 25327887

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Project description:Disease prediction tools improve management efforts for many plant diseases. Prediction and downstream prevention demand information about disease etiology, which can be complicated for some diseases, like those caused by soilborne microorganisms. Fortunately, the availability of machine learning methods has enabled researchers to elucidate complex relationships between hosts and pathogens without invoking difficult-to-satisfy assumptions. The etiology of a destructive plant disease, Verticillium wilt of mint, caused by the fungus Verticillium dahliae was reevaluated with several supervised machine learning methods. Specifically, the objective of this research was to identify drivers of wilt in commercial mint fields, describe the relationships between these drivers, and predict wilt. Soil samples were collected from commercial mint fields. Wilt foci, V. dahliae, and plant-parasitic nematodes that can exacerbate wilt were quantified. Multiple linear regression, a generalized additive model, random forest, and an artificial neural network were fit to the data, validated with 10-fold cross-validation, and measures of explanatory and predictive performance were compared. All models selected nematodes within the genus Pratylenchus as the most important predictor of wilt. The fungus after which this disease is named, V. dahliae, was the fourth most important predictor of wilt, after crop age and cultivar. All models explained around 50% of the total variation (R2 ≤ 0.46), and exhibited comparable predictive error (RMSE ≤ 1.21). Collectively, these models revealed that the quantitative relationships between two pathogens, mint cultivars and age are required to explain wilt. The ascendance of Pratylenchus spp. in predicting symptoms of a disease assumed to primarily be caused by V. dahliae exposes the underestimated contribution of these nematodes to wilt. This research provides a foundation on which predictive forecasting tools can be developed for mint growers and reminds us of the lessons that can be learned by revisiting assumptions about disease etiology.

Dataset Information

Assembly of complex plant-fungus networks.

Publications

Assembly of complex plant-fungus networks.

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