Colonization-persistence trade-offs in natural bacterial communities.

Fitness equalizing mechanisms, such as trade-offs, are recognized as one of the main factors promoting species coexistence in community ecology. However, they have rarely been explored in microbial communities. Although microbial communities are highly diverse, the coexistence of their multiple taxa is largely attributed to niche differences and high dispersal rates, following the principle 'everything is everywhere, but the environment selects'. We use a dynamical stochastic model based on the theory of island biogeography to study highly diverse bacterial communities over time across three different systems (soils, alpine lakes and shallow saline lakes). Assuming fitness equalization mechanisms, here we newly analytically derive colonization-persistence trade-offs, and report a signal of such trade-offs in natural bacterial communities. Moreover, we show that different subsets of species in the community drive this trade-off. Rare taxa, which are occasional and more likely to follow independent colonization/extinction dynamics, drive this trade-off in the aquatic communities, while the core sub-community did it in the soils. We conclude that equalizing mechanisms may be more important than previously recognized in bacterial communities. Our work also emphasizes the fundamental value of dynamical models for understanding temporal patterns and processes in highly diverse communities.

Biodiversity time series are biased towards increasing species richness in changing environments.

The discrepancy between global loss and local constant species richness has led to debates over data quality, systematic biases in monitoring programmes and the adequacy of species richness to capture changes in biodiversity. We show that, more fundamentally, null expectations of stable richness can be wrong, despite independent yet equal colonization and extinction. We analysed fish and bird time series and found an overall richness increase. This increase reflects a systematic bias towards an earlier detection of colonizations than extinctions. To understand how much this bias influences richness trends, we simulated time series using a neutral model controlling for equilibrium richness and temporal autocorrelation (that is, no trend expected). These simulated time series showed significant changes in richness, highlighting the effect of temporal autocorrelation on the expected baseline for species richness changes. The finite nature of time series, the long persistence of declining populations and the potential strong dispersal limitation probably lead to richness changes when changing conditions promote compositional turnover. Temporal analyses of richness should incorporate this bias by considering appropriate neutral baselines for richness changes. Absence of richness trends over time, as previously reported, can actually reflect a negative deviation from the positive biodiversity trend expected by default.

Multilayer networks of plasmid genetic similarity reveal potential pathways of gene transmission.

Antimicrobial resistance (AMR) is a significant threat to public health. Plasmids are principal vectors of AMR genes, significantly contributing to their spread and mobility across hosts. Nevertheless, little is known about the dynamics of plasmid genetic exchange across animal hosts. Here, we use theory and methodology from network and disease ecology to investigate the potential of gene transmission between plasmids using a data set of 21 plasmidomes from a single dairy cow population. We constructed a multilayer network based on pairwise plasmid genetic similarity. Genetic similarity is a signature of past genetic exchange that can aid in identifying potential routes and mechanisms of gene transmission within and between cows. Links between cows dominated the transmission network, and plasmids containing mobility genes were more connected. Modularity analysis revealed a network cluster where all plasmids contained a mobM gene, and one where all plasmids contained a beta-lactamase gene. Cows that contain both clusters also share transmission pathways with many other cows, making them candidates for super-spreading. In support, we found signatures of gene super-spreading in which a few plasmids and cows are responsible for most gene exchange. An agent-based transmission model showed that a new gene invading the cow population will likely reach all cows. Finally, we showed that edge weights contain a non-random signature for the mechanisms of gene transmission, allowing us to differentiate between dispersal and genetic exchange. These results provide insights into how genes, including those providing AMR, spread across animal hosts.

Understanding stochastic and deterministic assembly processes in microbial communities along temporal, spatial and environmental scales.

Identifying the main drivers of community assembly remains an open fundamental question in ecology. Dispersal processes introduce randomness in community composition while selection for particular environments creates predictable assemblages. However, the interaction between selection and dispersal processes is still poorly understood. Here, we address this question in bacterial and microeukaryotic communities inhabiting a highly dynamic system of ephemeral (hyper)saline lakes. We show that the combination of beta-diversity decomposition methods and a temporal approach based on colonization and extinction dynamics yields new insights into the relative effect of selection and dispersal along environmental gradients. Selective pressure and dispersal-related processes simultaneously shape each local community with variable strength and effect. The dominance of selection vs. dispersal shifted from stochastic to deterministic assembly as salinity increased along the gradient. This transition also had an impact on the temporal dynamics of the lakes as community turnover decreased at high salinities because both colonization and extinction rates slowed down. Only microeukaryotic richness decreased along the gradient due to lower effective colonization at higher salinities, suggesting that the net effect of selection and dispersal is determined by both environmental conditions and the idiosyncrasy of the different microbial ecologies. Our results emphasize the use of temporal approaches in combination with standard statistical methods for a better understanding of the dynamic processes underlying community assembly.

Absence of stress-promoted facilitation coupled with a competition decrease in the microbiome of ephemeral saline lakes.

Salinity fluctuations constitute a well-known high stress factor strongly shaping global biological distributions and abundances. However, there is a knowledge gap regarding how increasing saline stress affects microbial biological interactions. We applied the combination of a probabilistic method for estimating significant co-occurrences/exclusions and a conceptual framework for filtering out associations potentially linked to environmental and/or spatial factors, in a series of connected ephemeral (hyper) saline lakes. We carried out a network analysis over the full aquatic microbiome-bacteria, eukarya, and archaea-under severe salinity fluctuations. Most of the observed co-occurrences/exclusions were potentially explained by environmental niche and/or dispersal limitation. Co-occurrences assigned to potential biological interactions remained stable, suggesting that the salt gradient was not promoting interspecific facilitation processes. Conversely, co-exclusions assigned to potential biological interactions decreased along the gradient both in number and network complexity, pointing to a decrease of interspecies competition as salinity increased. Overall, higher saline stress reduced microbial co-exclusions while co-occurrences remained stable suggesting decreasing competition coupled with lack of stress-gradient promoted facilitation in the microbiome of ephemeral saline lakes.

Biological Microbial Interactions from Cooccurrence Networks in a High Mountain Lacustrine District.

A fundamental question in biology is why some species tend to occur together in the same locations, while others are never observed coexisting. This question becomes particularly relevant for microorganisms thriving in the highly diluted waters of high mountain lakes, where biotic interactions might be required to make the most of an extreme environment. We studied a high-throughput gene data set of alpine lakes (>220 Pyrenean lakes) with cooccurrence network analysis to infer potential biotic interactions, using the combination of a probabilistic method for determining significant cooccurrences and coexclusions between pairs of species and a conceptual framework for classifying the nature of the observed cooccurrences and coexclusions. This computational approach (i) determined and quantified the importance of environmental variables and spatial distribution and (ii) defined potential interacting microbial assemblages. We determined the properties and relationships between these assemblages by examining node properties at the taxonomic level, indicating associations with their potential habitat sources (i.e., aquatic versus terrestrial) and their functional strategies (i.e., parasitic versus mixotrophic). Environmental variables explained fewer pairs in bacteria than in microbial eukaryotes for the alpine data set, with pH alone explaining the highest proportion of bacterial pairs. Nutrient composition was also relevant for explaining association pairs, particularly in microeukaryotes. We identified a reduced subset of pairs with the highest probability of species interactions ("interacting guilds") that significantly reached higher occupancies and lower mean relative abundances in agreement with the carrying capacity hypothesis. The interacting bacterial guilds could be more related to habitat and microdispersal processes (i.e., aquatic versus soil microbes), whereas for microeukaryotes trophic roles (osmotrophs, mixotrophs, and parasitics) could potentially play a major role. Overall, our approach may add helpful information to guide further efforts for a mechanistic understanding of microbial interactions in situ. IMPORTANCE A fundamental question in biology is why some species tend to occur together in the same locations, while others are never observed to coexist. This question becomes particularly relevant for microorganisms thriving in the highly diluted waters of high mountain lakes, in which biotic interactions might be required to make the most of an extreme environment. Microbial metacommunities are too often only studied in terms of their environmental niches and geographic barriers since they show inherent difficulties to quantify biological interactions and their role as drivers of ecosystem functioning. Our study highlights that telling apart potential interactions from both environmental and geographic niches may help for the initial characterization of organisms with similar ecologies in a large scope of ecosystems, even when information about actual interactions is partial and limited. The multilayered statistical approach carried out here offers the possibility of going beyond taxonomy to understand microbiological behavior in situ.

General decline in the diversity of the airborne microbiota under future climatic scenarios.

Microorganisms attached to aerosols can travel intercontinental distances, survive, and further colonize remote environments. Airborne microbes are influenced by environmental and climatic patterns that are predicted to change in the near future, with unknown consequences. We developed a new predictive method that dynamically addressed the temporal evolution of biodiversity in response to environmental covariates, linked to future climatic scenarios of the IPCC (AR5). We fitted these models against a 7-year monitoring of airborne microbes, collected in wet depositions. We found that Bacteria were more influenced by climatic variables than by aerosols sources, while the opposite was detected for Eukarya. Also, model simulations showed a general decline in bacterial richness, idiosyncratic responses of Eukarya, and changes in seasonality, with higher intensity within the worst-case climatic scenario (RCP 8.5). Additionally, the model predicted lower richness for airborne potential eukaryotic (fungi) pathogens of plants and humans. Our work pioneers on the potential effects of environmental variability on the airborne microbiome under the uncertain context of climate change.

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

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.

The characteristic time of ecological communities.

A simple description of temporal dynamics of ecological communities may help us understand how community assembly proceeds, predict ecological responses to environmental disturbances, and improve the performance of biological conservation actions. Although community changes take place at multiple temporal scales, the variation of species composition and richness over time across communities and habitats shows general patterns that may potentially reveal the main drivers of community dynamics. We used the simplest stochastic model of island biogeography to propose two quantities to characterize community dynamics: the community characteristic time, as a measure of the typical time scale of species-richness change, and the characteristic Jaccard index, as a measure of temporal ß diversity, that is, the variation of community composition over time. In addition, the community characteristic time, which sets the temporal scale at which null, noninteracting species assemblages operate, allowed us to define a relative sampling frequency (to the characteristic time). Here we estimate these quantities across microbial and macroscopic species assemblages to highlight two related results. First, we illustrated both characteristic time and Jaccard index and their relation with classic time-series in ecology, and found that the most thoroughly sampled communities, relative to their characteristic time, presented the largest similarity between consecutive samples. Second, our analysis across a variety of habitats and taxa show that communities span a large range of species turnover, from potentially very fast (short characteristic times) to rather slow (long characteristic times) communities. This was in agreement with previous knowledge, but indicated that some habitats may have been sampled less frequently than required. Our work provides new perspectives to explore the temporal component in ecological studies and highlights the usefulness of simple approximations to the complex dynamics of ecological communities.

Dynamics and ecological distributions of the Archaea microbiome from inland saline lakes (Monegros Desert, Spain).

We characterized the rich Archaea microbiome of shallow inland lakes (Monegros Desert, NE Spain) by 16S rRNA gene tag sequencing covering a wide salinity range (0.1%-40% w/v) along 3 years. Up to 990 operational taxonomic units (OTUs; >97% identity) were detected allocated in 14 major archaeal phyla and heterogeneously distributed along the salt gradient. Dynamics and idiosyncratic ecological distributions were uncovered for the different phyla. A high genetic richness was observed for Woesearchaeota and Pacearchaeota (>370 OTUs each), followed by Halobacteria (105), Nanohaloarchaeota (62) and Thermoplasmata (19). Overall, the distribution of genetic richness was strongly correlated with environmental niche amplitude, but not with occurrence. We unveiled high occurrence for a very rich Woesearchaeota assemblage, and an unexpected positive correlation of Pacearchaeota abundance with salinity at >15% dissolved salt content. The estimated dynamic behaviour (temporal 'turnover' rates of presence/absence data) unveiled Thaumarchaeota and Halobacteria as the most dynamic groups, and Aenigmarchaeota and Thermoplasmata as the most stable. The DPANN Pacearchaeota, Woesearchaeota, and Nanohaloarchaeota showed intermediate rates, suggesting higher resilience to environmental perturbations. A rich and dynamic Archaea microbiome was unveiled, including unseen ecological traits for relevant members of the still largely unknown DPANN group, supporting a strong ecological differentiation between Pacearchaeota and Woesearchaeota.

A Randomized Trait Community Clustering approach to unveil consistent environmental thresholds in community assembly.

Similarities and differences of phenotypes within local co-occurring species hold the key to inferring the contribution of stochastic or deterministic processes in community assembly. Developing both phylogenetic-based and trait-based quantitative methods to unravel these processes is a major aim in community ecology. We developed a trait-based approach that: (i) assesses if a community trait clustering pattern is related to increasing environmental constraints along a gradient; and (ii) determines quantitative thresholds for an environmental variable along a gradient to interpret changes in prevailing community assembly drivers. We used a regional set of natural shallow saline ponds covering a wide salinity gradient (0.1-40% w/v). We identify a consistent discrete salinity threshold (ca. 5%) for microbial community assembly drivers. Above 5% salinity a strong environmental filtering prevailed as an assembly force, whereas a combination of biotic and abiotic factors dominated at lower salinities. This method provides a conceptual approach to identify consistent environmental thresholds in community assembly and enables quantitative predictions for the ecological impact of environmental changes.