Network Properties of Local Fungal Communities Reveal the Anthropogenic Disturbance Consequences of Farming Practices in Vineyard Soils

Abstract

Agroecosystems are human-managed ecosystems subject to generalized ecological rules. Understanding the ecology behind the assembly and dynamics of soil fungal communities is a fruitful way to improve management practices and plant productivity. Thus, monitoring soil health would benefit from the use of metrics that arise from ecological explanations that can also be informative for agricultural management. Beyond traditional biodiversity descriptors, community-level properties have the potential of informing about particular ecological situations. Here we assess the impact of different farming practices in a survey of 350 vineyard soils from the United States and Spain by estimating network properties based on spatial associations. Our observations using traditional approaches show results concurring with previous literature: the influence of geographic and climatic factors on sample distributions, or different operational taxonomic unit (OTU) compositions depending on agricultural managements. Furthermore, using network properties, we observe that fungal communities ranged from dense arrangements of associations to a sparser structure of associations, indicating differential levels of niche specialization. We detect fungal arrangements capable of thriving in wider or smaller ranges of temperature, revealing that niche specialization may be a critical soil process impacting soil health. Low-intervention practices (organic and biodynamic managements) promoted densely clustered networks, describing an equilibrium state based on mixed collaborative communities. In contrast, conventionally managed vineyards had highly modular sparser communities, supported by a higher coexclusion proportion. Thus, we hypothesize that network properties at the community level may help to understand how the environment and land use can affect community structure and ecological processes in agroecosystems. IMPORTANCE Soil fungal communities play a key role in agroecosystem sustainability. The complexity of fungal communities, at both taxonomic and functional levels, makes it difficult to find clear patterns connecting community composition with ecosystem function and to understand the impact of biotic (interspecies interactions) and abiotic (e.g., climate or anthropogenic disturbances) factors on it. Here we combine network analysis methods and properties, proposing a novel analytical approach: to infer ecological properties from local networks, which we apply to the study of fungal communities in vineyard soils. We conclude that different levels of farming intensification may lead to different ecological strategies in soil fungal communities settled by particular association arrangements.

Local Network Properties of Soil and Rhizosphere Microbial Communities in Potato Plantations Treated with a Biological Product Are Important Predictors of Crop Yield

“…In order to get a deeper understanding of how the structure of the bacterial and fungal communities, and therefore the ecological relationships among microorganisms, impacts the effect of the bacterial inoculant, we studied the cooccurrence and coexclusion patterns between pairs of OTUs in each sample of the trial. As some of us reported in a recent work ( 40 ), by studying the network properties of local communities inferred from the cooccurrences and coexclusion patterns of a reference metacommunity it is possible to estimate ecological emergent properties (i.e., niche specialization, level of competition) of interest for the understanding of microbiome functioning ( 41 ). We first built metacommunities based on all samples of the trial and then explored the structure of local microbiome communities, based on just the nodes present in each individual sample, aiming to detect changes in network properties that are associated with the application of the biological product at a specific location over time.…”

“…Metacommunity networks were built for 16S and ITS data separately using the methods described by Veech ( 57 ) and Ortiz-Álvarez et al ( 40 ). In a nutshell, we first built a metacommunity network of all samples: this was done by estimating the cooccurrence and coexclusion that would occur solely by chance for all possible OTU pairs, given the data.…”

Niche differentiation drives microbial community assembly and succession in full-scale activated sludge bioreactors

“…In both WWTPs, clustering coefficient showed a negative correlation with modularity and co-exclusion proportion (Supplementary Table 2 ). These relationships may suggest a conserved response of low specialized and interconnected communities to environmental gradients in a succession towards a stronger niche specialization 9 , 17 . Thus, SU-WWTP microbiome assembly shows a transition between low modular and aggregated (clustered) communities, resembling small-world networks 18 , to highly modular low-clustered communities with differentiated niches.…”

“…Co-occurrence networks, applied to microbial communities, include every potential association between taxa in the studied system and allow for the assessment of putative affinities to habitat preferences or ecological niches of different phylotypes 8 . Local (timepoint) networks in individual samples can be inferred from the association patterns observed at the metaweb (microbiome) level, allowing for the estimation of their individual properties to better understand community structure and dynamics, and its interaction with environmental factors 9 . In addition, the ecological processes that govern community assembly can be categorized as deterministic (abiotic and biotic factors shape patterns of community diversity and composition), and stochastic processes (changes in community structure emerge from random events, assuming all the species within a community have the same fitness) 10 , 11 .…”

“…1 ). We adapted the framework developed by Ortiz-Álvarez et al 9 to estimate the network properties of individual samples in a temporal scale (timepoint samples) and thus, to describe the evolution of the structure and composition of microbial communities. Then, using the approach developed by Webb 14 , we characterized the phylogenetic dispersion of co-occurring bacteria to evaluate the local relative influence of stochastic and deterministic processes in community succession.…”