Soil microbial communities play a crucial role in ecosystem functioning, but it is unknown how co-occurrence networks within these communities respond to disturbances such as climate extremes This represents an important knowledge gap because changes in microbial networks could have implications for their functioning and vulnerability to future disturbances Here, we show in grassland mesocosms that drought promotes destabilising properties in soil bacterial, but not fungal, co-occurrence networks, and that changes in bacterial communities link more strongly to soil functioning during recovery than do changes in fungal communities Moreover, we reveal that drought has a prolonged effect on bacterial communities and their co-occurrence networks via changes in vegetation composition and resultant reductions in soil moisture Our results provide new insight in the mechanisms through which drought alters soil microbial communities with potential long-term consequences, including future plant community composition and the ability of aboveground and belowground communities to withstand future disturbances

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Soil bacterial networks are less stable under drought than fungal networks.

The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term “microbiome.” Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role.

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Microbiome definition re-visited: old concepts and new challenges

Root-associated microbes play a key role in plant performance and productivity, making them important players in agroecosystems. So far, very few studies have assessed the impact of different farming systems on the root microbiota and it is still unclear whether agricultural intensification influences the structure and complexity of microbial communities. We investigated the impact of conventional, no-till, and organic farming on wheat root fungal communities using PacBio SMRT sequencing on samples collected from 60 farmlands in Switzerland. Organic farming harbored a much more complex fungal network with significantly higher connectivity than conventional and no-till farming systems. The abundance of keystone taxa was the highest under organic farming where agricultural intensification was the lowest. We also found a strong negative association (R2 = 0.366; P < 0.0001) between agricultural intensification and root fungal network connectivity. The occurrence of keystone taxa was best explained by soil phosphorus levels, bulk density, pH, and mycorrhizal colonization. The majority of keystone taxa are known to form arbuscular mycorrhizal associations with plants and belong to the orders Glomerales, Paraglomerales, and Diversisporales. Supporting this, the abundance of mycorrhizal fungi in roots and soils was also significantly higher under organic farming. To our knowledge, this is the first study to report mycorrhizal keystone taxa for agroecosystems, and we demonstrate that agricultural intensification reduces network complexity and the abundance of keystone taxa in the root microbiome.

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Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots

There is a rich amount of information in co-occurrence (presence-absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co-occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co-occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co-occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co-occurrence, along with potential avenues to extract as much information as possible from such data.

Co-occurrence is not evidence of ecological interactions

Microbial networks are an increasingly popular tool to investigate microbial community structure, as they integrate multiple types of information and may represent systems-level behaviour. Interpreting these networks is not straightforward, and the biological implications of network properties are unclear. Analysis of microbial networks allows researchers to predict hub species and species interactions. Additionally, such analyses can help identify alternative community states and niches. Here, we review factors that can result in spurious predictions and address emergent properties that may be meaningful in the context of the microbiome. We also give an overview of studies that analyse microbial networks to identify new hypotheses. Moreover, we show in a simulation how network properties are affected by tool choice and environmental factors. For example, hub species are not consistent across tools, and environmental heterogeneity induces modularity. We highlight the need for robust microbial network inference and suggest strategies to infer networks more reliably.

From hairballs to hypotheses-biological insights from microbial networks.

Co-occurrence methods are increasingly utilized in ecology to infer networks of species interactions where detailed knowledge based on empirical studies is difficult to obtain. Their use is particularly common, but not restricted to, microbial networks constructed from metagenomic analyses. In this study, we test the efficacy of this procedure by comparing an inferred network constructed using spatially intensive co-occurrence data from the rocky intertidal zone in central Chile to a well-resolved, empirically based, species interaction network from the same region. We evaluated the overlap in the information provided by each network and the extent to which there is a bias for co-occurrence data to better detect known trophic or non-trophic, positive or negative interactions. We found a poor correspondence between the co-occurrence network and the known species interactions with overall sensitivity (probability of true link detection) equal to 0.469, and specificity (true non-interaction) equal to 0.527. The ability to detect interactions varied with interaction type. Positive non-trophic interactions such as commensalism and facilitation were detected at the highest rates. These results demonstrate that co-occurrence networks do not represent classical ecological networks in which interactions are defined by direct observations or experimental manipulations. Co-occurrence networks provide information about the joint spatial effects of environmental conditions, recruitment, and, to some extent, biotic interactions, and among the latter, they tend to better detect niche-expanding positive non-trophic interactions. Detection of links (sensitivity or specificity) was not higher for well-known intertidal keystone species than for the rest of consumers in the community. Thus, as observed in previous empirical and theoretical studies, patterns of interactions in co-occurrence networks must be interpreted with caution, especially when extending interaction-based ecological theory to interpret network variability and stability. Co-occurrence networks may be particularly valuable for analysis of community dynamics that blends interactions and environment, rather than pairwise interactions alone.

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Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities?

Author Posting. © The Oceanography Society, 2016. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 29, no. 2 (2016): 72–81, doi:10.5670/oceanog.2016.40.

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Freshwater in the Bay of Bengal Its Fate and Role in Air-Sea Heat Exchange

The structure and variability of upper-ocean properties in the Bay of Bengal (BoB) modulate air-sea interactions, which profoundly influence the pattern and intensity of monsoonal precipitation across the Indian subcontinent. In turn, the bay receives a massive amount of freshwater through river input at its boundaries and from heavy local rainfall, leading to a salinity-stratified surface ocean and shallow mixed layers. Small-scale oceanographic processes that drive variability in near-surface BoB waters complicate the tight coupling between ocean and atmosphere implicit in this seasonal feedback. Unraveling these ocean dynamics and their impact on air-sea interactions is critical to improving the forecasting of intraseasonal variability in the southwest monsoon. To that end, we deployed a wave-powered, rapidly profiling system capable of measuring the structure and variability of the upper 100 m of the BoB. The evolution of upper-ocean structure along the trajectory of the instrument's roughly two-week drift, along with direct estimates of vertical fluxes of salt and heat, permit assessment of the contributions of various phenomena to temporal and spatial variability in the surface mixed layer depth. Further, these observations suggest that the particular ``barrier-layer'' stratification found in the BoB may decrease the influence of the wind on mixing processes in the interior, thus isolating the upper ocean from the interior below, and tightening its coupling to the atmosphere above.

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Adrift Upon a Salinity-Stratified Sea: A View of Upper-Ocean Processes in the Bay of Bengal During the Southwest Monsoon

The upper 200 m of the two northern Indian Ocean embayments, the Bay of Bengal (BoB) and the Arabian Sea (AS), differ sharply in their salinity stratification, as the Asian monsoon injects massive amounts of freshwater into the BoB while removing freshwater via evaporation from the AS. The ocean circulation transfers salt from the AS to the BoB and exports freshwater from the BoB to mitigate the salinity difference and reach a quasi-steady state, albeit with strong seasonality. An energetic field of mesoscale features and an intrathermocline eddy was observed within the BoB during the R/V Revelle November and December 2013 Air-Sea Interactions Regional Initiative cruises, marking the early northeast monsoon phase. Mesoscale features, which display a surprisingly large thermohaline range within their confines, obscure the regional surface water and thermohaline stratification patterns, as observed by satellite and Argo profilers. Ocean processes blend the fresh and salty features along and across density surfaces, influencing sea surface temperature and air-sea flux. Comparing the Revelle observations to the Argo data reveals a general westward migration of mesoscale features across the BoB.

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Bay of Bengal: 2013 northeast monsoon upper-ocean circulation

Within the pycnocline, where diapycnal mixing is suppressed, both the vertical movement (uplift) of isopycnal surfaces and upward motion along sloping isopycnals supply nutrients to the eup...

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Mara Freilich

Papers

Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities?

Freshwater in the Bay of Bengal Its Fate and Role in Air-Sea Heat Exchange

Adrift Upon a Salinity-Stratified Sea: A View of Upper-Ocean Processes in the Bay of Bengal During the Southwest Monsoon

Bay of Bengal: 2013 northeast monsoon upper-ocean circulation

Decomposition of Vertical Velocity for Nutrient Transport in the Upper Ocean