In an era of ecosystem degradation and climate change, maximizing microbial functions in agroecosystems has become a prerequisite for the future of global agriculture. However, managing species-rich communities of plant-associated microbiomes remains a major challenge. Here, we propose interdisciplinary research strategies to optimize microbiome functions in agroecosystems. Informatics now allows us to identify members and characteristics of ‘core microbiomes’, which may be deployed to organize otherwise uncontrollable dynamics of resident microbiomes. Integration of microfluidics, robotics and machine learning provides novel ways to capitalize on core microbiomes for increasing resource-efficiency and stress-resistance of agroecosystems.

Core microbiomes for sustainable agroecosystems.

Co-occurrence networks—ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques—are widely used in microbial ecology. Several co-occurrence network methods have been proposed. Co-occurrence network methods only infer ecological associations and are often used to discuss species interactions. However, validity of this application of co-occurrence network methods is currently debated. In particular, they simply evaluate using parametric statistical models, even though microbial compositions are determined through population dynamics. We comprehensively evaluated the validity of common methods for inferring microbial ecological networks through realistic simulations. We evaluated how correctly nine widely used methods describe interaction patterns in ecological communities. Contrary to previous studies, the performance of the co-occurrence network methods on compositional data was almost equal to or less than that of classical methods (e.g., Pearson’s correlation). The methods described the interaction patterns in dense and/or heterogeneous networks rather inadequately. Co-occurrence network performance also depended upon interaction types; specifically, the interaction patterns in competitive communities were relatively accurately predicted while those in predator–prey (parasitic) communities were relatively inadequately predicted. Our findings indicated that co-occurrence network approaches may be insufficient in interpreting species interactions in microbiome studies. However, the results do not diminish the importance of these approaches. Rather, they highlight the need for further careful evaluation of the validity of these much-used methods and the development of more suitable methods for inferring microbial ecological networks.

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Difficulty in inferring microbial community structure based on co-occurrence network approaches.

Nutrient supply is a key bottom-up control of phytoplankton primary production in lake ecosystems. Top-down control via grazing pressure by zooplankton also constrains primary production and primary production may simultaneously affect zooplankton. Few studies have addressed these bidirectional interactions. We used convergent cross-mapping (CCM), a numerical test of causal associations, to quantify the presence and direction of the causal relationships among environmental variables (light availability, surface water temperature, NO3 -N, and PO4 -P), phytoplankton community composition, primary production, and the abundances of five functional zooplankton groups (large cladocerans, small cladocerans, rotifers, calanoids, and cyclopoids) in Lake Kasumigaura, a shallow, hypereutrophic lake in Japan. CCM suggested that primary production was causally influenced by NO3 -N and phytoplankton community composition; there was no detectable evidence of a causal effect of zooplankton on primary production. Our results also suggest that rotifers and cyclopoids were forced by primary production, and cyclopoids were further influenced by rotifers. However, our CCM suggested that primary production was weakly influenced by rotifers (i.e., bidirectional interaction). These findings may suggest complex linkages between nutrients, primary production, and rotifers and cyclopoids, a pattern that has not been previously detected or has been neglected. We used linear regression analysis to examine the relationships between the zooplankton community and pond smelt (Hypomesus nipponensis), the most abundant planktivore and the most important commercial fish species in Lake Kasumigaura. The relative abundance of pond smelt was significantly and positively correlated with the abundances of rotifers and cyclopoids, which were causally influenced by primary production. This finding suggests that bottom-up linkages between nutrient, primary production, and zooplankton abundance might be a key mechanism supporting high planktivore abundance in eutrophic lakes. Because increases in primary production and cyanobacteria blooms are likely to occur simultaneously in hypereutrophic lakes, our study highlights the need for ecosystem management to resolve the conflict between good water quality and high fishery production.

Bottom-up linkages between primary production, zooplankton, and fish in a shallow, hypereutrophic lake

Changes in microbiota along the intestine of grass carp (Ctenopharyngodon idella): Community, interspecific interactions, and functions

1. Multivariate autoregressive (MAR) models are an increasingly popular technique to infer interaction strengths between species in a community and to predict the community response to environmental change. The most commonly employed MAR(1) models, with one time lag, can be viewed either as multispecies competition models with Gompertz density dependence or, more generally, as a linear approximation of more complex, nonlinear dynamics around stable equilibria. This latter interpretation allows for broader applicability, but may come at a cost in terms of interpretation of estimates and reliability of both short- and long-term predictions. 2. We investigate what these costs might be by fitting MAR(1) models to simulated 2-species competition, consumer-resource and host-parasitoid systems, as well as a larger food web influenced by the environment. We review how MAR(1) coefficients can be interpreted and evaluate how reliable are estimates of interaction strength, rank, or sign; accuracy of short-term forecasts; as well as the ability of MAR(1) models to predict the long-term responses of communities submitted to environmental change such as PRESS perturbations. 3. The net effects of species j on species i are usually (90%-95%) well recovered in terms of sign or rank, with the notable exception of overcompensatory dynamics. In actual values, net effects of species j on species i are not well recovered when the underlying dynamics are nonlinear. MAR(1) models are better at making short-term qualitative forecasts (next point going up or down) than at predicting long-term responses to environmental perturbations, which can be severely over- as well as underestimated. 4. We conclude that when applying MAR(1) models to ecological data, inferences on net effects among species should be limited to signs, or the Gompertz assumption should be tested and discussed. This particular assumption on density-dependence (log-linearity) is also required for unbiased long-term predictions. Overall, we think that MAR(1) models are highly useful tools to resolve and characterize community dynamics, but we recommend to use them in conjunction with alternative, nonlinear models resembling the ecological context in order to improve their interpretation in specific applications.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.13021

How do MAR(1) models cope with hidden nonlinearities in ecological dynamics

1.Mapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics.

2.We developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes.

3.We showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice.

4.The complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.

This article is protected by copyright. All rights reserved.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12814

An equation‐free method reveals the ecological interaction networks within complex microbial ecosystems

Mapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. In recent years various approaches have been used to reveal microbial interaction networks from metagenomic sequencing data, such as time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics. We developed the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. The advantage of the SSM over alternative methodologies is that it fully utilizes the observed data using a framework of empirical dynamic modelling. This makes the SSM robust to non-equilibrium dynamics and underlying complexity (nonlinearity) in microbial processes. We showed that an increase in dataset size or a decrease in observational error improved the accuracy of SSM whereas, the accuracy of a comparative equation-based method was almost unchanged for both cases and equivalent to the SSM at best. Hence, the SSM outperformed a comparative equation-based method when datasets were large and the magnitude of observational errors were small. The results were robust to the magnitude of process noise and the functional forms of inter-specific interactions that we tested. We applied the method to a microbiome data of six mice and found that there were different microbial interaction regimes between young to middle age (4-40 week-old) and middle to old age (36-72 week-old) mice. The complexity of microbial relationships impedes detailed equation-based modeling. Our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments and will be improved by further developments in metagenomics sequencing technologies leading to increased dataset size and improved accuracy and precision.

https://www.biorxiv.org/content/biorxiv/early/2017/03/31/080697.full.pdf

An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

Mapping the network of ecological interactions is key to understanding the composition, stability, function and dynamics of microbial communities. These ecosystem properties provide the mechanistic basis for understanding and designing microbial treatments that attempt to promote human health and provide environmental services. In recent years various approaches have been used to reveal microbial interaction networks, inferred from metagenomic sequencing data using time-series analysis, machine learning and statistical techniques. Despite these efforts it is still not possible to capture details of the ecological interactions behind complex microbial dynamics. Here, we develop the sparse S-map method (SSM), which generates a sparse interaction network from a multivariate ecological time-series without presuming any mathematical formulation for the underlying microbial processes. We show that this method outperforms a comparative equation-based method and that the results were robust to the range of observational errors and quantity of data that we tested. We then applied the method to the microbiome data of six mice and found that the mice had similar interaction networks when they were middle- to old-aged (36-72 week-old), characterized by the high connectivity of an unclassified Clostridiales. However, there was almost no shared network patterns when they were young- to middle-aged (4-36 week-old). The results shed light on the universality of microbial interactions during the lifelong dynamics of mouse gut-microbiota. The complexity of microbial relationships impede detailed equation-based modeling, and our method provides a powerful alternative framework to infer ecological interaction networks of microbial communities in various environments.

https://www.biorxiv.org/content/early/2016/10/13/080697.full.pdf

Katsuhiko Yoshida

Papers

An equation‐free method reveals the ecological interaction networks within complex microbial ecosystems

An equation-free method reveals the ecological interaction networks within complex microbial ecosystems

Liberation from equations: An equation-free method reveals the ecological interaction networks within complex microbial ecosystems