The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures.

Elroy Galbraith,Matteo Convertino

doi:10.3390/e23111471

Abstract

The microbiome emits informative signals of biological organization and environmental pressure that aid ecosystem monitoring and prediction. Are the many signals reducible to a habitat-specific portfolio that characterizes ecosystem health? Does an optimally structured microbiome imply a resilient microbiome? To answer these questions, we applied our novel Eco-Evo Mandala to bacterioplankton data from four habitats within the Great Barrier Reef, to explore how patterns in community structure, function and genetics signal habitat-specific organization and departures from theoretical optimality. The Mandala revealed communities departing from optimality in habitat-specific ways, mostly along structural and functional traits related to bacterioplankton abundance and interaction distributions (reflected by and as power law and exponential distribution parameters), which are not linearly associated with each other. River and reef communities were similar in their relatively low abundance and interaction disorganization (low and ) due to their protective structured habitats. On the contrary, lagoon and estuarine inshore reefs appeared the most disorganized due to the ocean temperature and biogeochemical stress. Phylogenetic distances (D) were minimally informative in characterizing bacterioplankton organization. However, dominant populations, such as Proteobacteria, Bacteroidetes, and Cyanobacteria, were largely responsible for community patterns, being generalists with a large functional gene repertoire (high D) that increases resilience. The relative balance of these populations was found to be habitat-specific and likely related to systemic environmental stress. The position on the Mandala along the three fundamental traits, as well as fluctuations in this ecological state, conveys information about the microbiome’s health (and likely ecosystem health considering bacteria-based multitrophic dependencies) as divergence from the expected relative optimality. The Eco-Evo Mandala emphasizes how habitat and the microbiome’s interaction network topology are first- and second-order factors for ecosystem health evaluation over taxonomic species richness. Unhealthy microbiome communities and unbalanced microbes are identified not by macroecological indicators but by mapping their impact on the collective proportion and distribution of interactions, which regulates the microbiome’s ecosystem function.

Highlights

We propose that the geomorphology, hydrodynamics, and water quality of these habitats may be responsible for the differentiation of microbial communities, which enables the identification of habitat class from community dynamics
In response to the question about how to characterize microbiome health, we found that the collective distribution of abundance and interactions is sufficient to assess the habitat-specific microbiome state, and even to predict habitat type from bacterioplankton organization
We found that a structurally stable microbiome does not equate to a dynamically stable one

Summary

Introduction

The Anthropocene presents an existential threat to many ecosystems, including marine habitats, necessitating ecological indicators that can assist in the prediction and management of environmental pressure. Global research on the Earth’s oceans [1,2,3,4,5] has revealed the large abundance, complexity, and ecological importance of microscopic organisms, which interact in communities called the microbiome. There is growing interest in the dynamics of microbiomes for indicating and predicting ecosystem health [11,12,13,14]. There is agreement that properly decoding marine microbiome signals (or those of any other aquatic ecosystem) is a critical first step in the assessment and consequent preservation and improvement of the resilience of ecosystems [15,16]. While there has been much success in the search for unique species (populations) predictive of ecosystem health states, more work is required to understand how the collective dynamics of the community can indicate ecosystem health, and there is value in understanding these dynamics within localities and regions [16], not merely at the global scale

Methods

Results

Discussion

Conclusion